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[Objectives] To achieve real-time and accurate counting of wheat ears in the field, this paper proposed a wheat ear detection and counting method based on improved YOLOv5s. [Methods] By using the C2f module to obtain richer gradient flows, the model's ability to extract fine-grained features was enhanced, and CoordConv coordinate convolution was introduced in key parts of the network to increase attention to coordinate information and improve the model's perception of wheat ear position. At the same time, considering that wheat ear detection tasks mainly involved small and medium-sized wheat ears, the Inner CIoU loss function was adopted to accelerate the convergence speed of the model. [Results] The above methods were tested on the publicly available dataset Global Wheat Head Detection(GWHD), and the results showed that the accuracy, recall, and average accuracy mean mAP0.5 of the proposed model were 93.5%, 91.6%, and 95.9%, respectively. The parameter count, computational complexity, and frames per second were 12.4 MB, 27.5 GFLOPs, and 34, respectively. [Conclusions] The model proposed in this article improved accuracy, recall, and average precision mean mAP0.5 by 1.0, 1.2, and 1.3 percentage points respectively compared to the original YOLOv5s model, and was superior to YOLOv7 tiny, YOLOv8s, and other models, while meeting the real-time detection requirements. In addition, the improved model performed better than the original model in dealing with complex situations such as occlusion and overlap, and had good robustness. [ABSTRACT FROM AUTHOR]

Background With the aging population, the incidence of pressure injury (PI) is gradually increasing. This not only severely impacts the quality of life for patients but also increases healthcare expenditures. However, the early detection and accurate staging of PI heavily depend on specialized training. Objective To construct and validate an artificial intelligence model for the automatic detection and staging of PI aimed at enhancing the real-time nature, accuracy, and objectivity of PI diagnostics. Methods A total of 693 PI images from the electronic management system of pressure ulcers at Changshu No.1 People's Hospital were selected from January 2021 to February 2024, the images were randomly divided into a training set (551 images) and a test set (142 images), and categorized into six stages according to National Pressure Ulcer Advisory Panel (NPUAP) guidelines: StageⅠ (154 images), StageⅡ (188 images), StageⅢ (160 images), StageⅣ (82 images), deep tissue injury (57 images), and unstageable (52 images). A deep learning object detection model for PI was established using five different versions of the YOLOv8 [nano (n), small (s), medium (m), large (l) and extra large (x) ] neural network and transfer learning. The model evaluation metrics included accuracy, sensitivity, specificity, false positive rate, and detection speed. Finally, the model was deployed to a mobile application via the Ultralytics Hub platform, facilitating the application of the AI model in clinical practice. Results During the evaluation of a test set containing 142 PI images, the YOLOv8l version demonstrated high accuracy (0.827) and fast inference speed (68.49 fps), achieving the best balance between precision and speed among the YOLO versions. Specifically, it achieved an overall accuracy of 93.18% across all categories, a sensitivity of 76.52%, a specificity of 96.29%, and a false positive rate of 3.72%. Among the six stages of PI, the model achieved the highest accuracy for StageⅠat 95.97%. The accuracies for StageⅡ, StageⅢ, StageⅣ, deep tissue injury, and unstageable were 91.28%, 91.28%, 91.95%, 95.30%, and 93.29%, respectively. In terms of processing speed, YOLOv8l took a total of 2.07 seconds to process 142 images, averaging 68.49 PI images per second. Conclusion The AI model based on the YOLOv8l network can quickly and accurately detect and stage PI. Deploying this model to a mobile app allows for portable use in clinical practice, demonstrating significant potential for clinical application.

In order to solve the problems of weak detection ability of small target defects of power equipment, high false detection and missed detection rate, and insufficient shallow network semantic information in traditional inspection image detection methods, a feature fusion enhanced detection method for small target defects of power equipment (multi-scale feature fusion enhanced you only look once, MFFE-YOLO) is proposed. This method designs a multi-scale feature fusion enhancement mechanism (multi-scale feature fusion enhancement, MFFE), which can capture target features more comprehensively. The study shows that embedding the cross-space learning multi-scale attention mechanism EMA and FasterNet Block in the C2f-EF module can optimize the operation efficiency of the model; the average accuracy, parameter amount and frame rate indicators of the MFFE-YOLO method are better than that of other methods, and can achieve a good balance between high accuracy and real-time performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

: Accurate detection of immature green citrus fruits in trees is one of the most critical steps for production decisions, such as early yield prediction, precise water and fertilizer management, and regulation of number of fruits hanging. However, the large model has been confined to identifying the immature citrus, due to the similar green citrus to the canopy background. Great challenges have been brought to rapidly and accurately detect the fruits, even to deploy the model. In this study, an improved model, YOLO-GC (You Only Look Once-Green Citrus) was proposed to detect the green citrus fruits using YOLOv5s. The improved model was also deployed into the edge mobile devices, in order to achieve the real-time and convenient detection of green citrus fruits in trees. Firstly, the original backbone network was replaced with a lightweight GhostNet one, because the YOLOv5s network model was large and difficult to deploy. Meanwhile, the attention was then improved to the green citrus features. Accuracy degradation was reduced after the model was lightweight. Global Attention Mechanism (GAM) was embedded in the backbone network and the feature fusion layer, in order to extract the fruit features in complex environments. Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture was introduced into the feature fusion layer for the multi-scale weighted feature fusion, in order to improve the dense and small-targeted fruits. Finally, the GIoU (Generalized Intersection over Union) loss function combined with Soft-NMS (Soft-Non-Maximum Suppression) was used to optimize the bounding box regression, in order to reduce the omission caused by the occlusion and overlapping of fruits and branches. The experimental results showed that the weight model memory of YOLO-GC was reduced by 53.9%, compared with the YOLOv5s. The number of parameters and the average inference time were reduced by 55.2% and 46.2%, respectively, whereas, the average precision (AP0.5) was improved by 1.2 percentage points. There was a lower amount of fruit leakage and misdetection in a variety of complex natural environments. The comprehensive performance of the YOLO-GC model was superior to that of CenterNet and seven commonly used network models, such as the YOLOv5s, YOLOv7, YOLOX, YOLOv8, CenterNet, Faster R-CNN, and RetinaNet target networks. The average accuracy of the YOLO-GC model was improved by 1.2, 1.3, 1.4, 0.9, 1.7, 4.6, and 3.9 percentage points, respectively, only 6.69 MB of weighted memory, thus achieving 97.6%, 90.3%, 97.8%, and 97.0% for the precision, recall, average precision, and F1 score, respectively. The YOLOGC model was then deployed to the Android mobile App for testing. The detection accuracy reached 97.2%, which was 2.4 percentage points higher than that of the YOLOv5s. Furthermore, the inference duration (103ms) was reduced by 85.8%. The IC model fully met the requirements of high-accuracy recognition and real-time inference of green citrus on the Android phone side. The finding can provide technical support to detect the green-like fruits in complex environments. The improved model was also deployed in the edge intelligent devices. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Potatoes have been a versatile food and cash crop to ensure food security and grain planting structure in China. The total planting area has declined in the planting industries at present. Some challenges also remain in the mechanization of the potato planting industry. The current process of seed potato cutting can rely heavily on manual and mechanical blind cutting, leading to labor-intensive and inefficient tasks. Moreover, there is a high rate of blind cuts and significant loss of seed potatoes. Therefore, it is highly urgent to accurately and rapidly identify the potato eyes before cutting. In this study, an improved model was proposed to detect the potato bud eyes using YOLOv5. Dutch 15 potato variety was taken as the experimental material. The high-quality samples of seed potatoes were carefully chosen to be free of diseases, dry rot, disease spots, and worm eyes. The dataset was used for training, verification, and testing. 1 400 pictures of seed potatoes were obtained with a ratio of 8:1:1. Data expansion techniques (such as mirroring, rotating, cropping, and adjusting brightness) were applied to enhance the dataset, thus resulting in a total of 5 600 images. Since the features of seed potato eyes were relatively simple, there was a decrease to even disappear after multiple convolutions. C3 Faster was integrated into the original framework. While the extraction of sprout eye features was enhanced to reduce the parameters. Additionally, the GD structure was incorporated from the Neck component of GOLD-YOLO to improve the detection accuracy of sprout eye. The bounding box loss function CIoU Loss was replaced with WIoU Loss to expedite the convergence of the network model for high detection accuracy. The hyperparameters of the original YOLO model were optimized for the COCO dataset. There were significant differences from the dataset of seed potato sprout eye in this experiment. Therefore, a genetic algorithm (GA) was employed to fine-tune the hyperparameters specifically for the detection of seed potato sprout eye at the end of the experiment. Furthermore, pruning and distillation techniques were used to reduce the running parameters and memory consumption, in order to make the model more suitable for the detection tasks of seed potato bud eye. The optimized model was presented with a size of 8.7 MB, which was only 61.3% of the original model. Params comprised approximately 57.1% of the original model. The final average accuracies of detection were 90.5% and 90.1%, respectively, in the test and validation sets of the self-made potato dataset. Compared with the lightweight networks YOLOv7-tiny, YOLOv8n, YOLOv5n, and YOLOv5s, the average accuracies of the improved model were 0.5, 1.3, 2.8, and 1.1 percentage points higher, respectively, in the test set of seed potato dataset. The average accuracy of the verification set was 2.9, 1.9, 3.2, and 1.6 percentage points higher. The detection speed on a local computer reached 27.5 frames per second, fully meeting the real-time requirements. Substantial benefits were offered to significantly enhance the efficiency and accuracy of seed potato bud eye detection in the potato cultivation industry. [ABSTRACT FROM AUTHOR]

Early growth of rapeseed can be used to assess the performance of seeder, grain yield, crop management, and fertilizer application. Rapid and accurate detection is critical to rapeseed production and yield. However, the manual seedling survey cannot fully meet the operational demands for the extensive or frequent high-precision seedling, due to the low efficiency and subjectivity. In this study, efficient detection and counting models were proposed for the video stream of rapeseed seedlings using unmanned aerial vehicle (UAV) imagery and machine learning. Multi-head self-attention was added to the YOLO series models. The attention was then reduced to irrelevant semantic information. The model was improved to focus on the target object. The basic receptive field block (BasicRFB) module was selected to replace the original spatial pyramid pooling-fast (SPPF) module. One-dimensional convolution was added to the Neck part. The downsampling was then changed to achieve the target of rapeseed seedlings in the image. The efficient fusion of features was also promoted to focus on the key features among other interference factors. The deep simple online and real-time tracking (DeepSORT) was further combined with the cross-line counting to achieve the continuous tracking and target number counting. In addition, the counting model was deployed on edge computing devices. A real-time target counting was designed using a multi-rotor UAV platform. The edge computing device was used to realize the real-time detection and counting of rape seedlings. The targets of rape seedlings were processed in the video stream that was captured by the camera in real time. The experimental results show that: 1) The improved model with multi-head self-attention was significantly focused on the rape seedling area in the image. A better performance was achieved in extracting the target features than before. 2) The detection accuracy of rapeseed seedlings was improved using BasicRFB and the operator of the Neck part. The detection misjudgment of targets was reduced to effectively alleviate the negative impact of invalid targets in the image background. 3) The improved YOLOv5s was achieved in the AP50 and AP95 scores of 93.1% and 67.5%, respectively. Among them, AP50 was significantly higher by 14.82%, 26.37%, and 3.3%, respectively, while AP95 was higher by 25.7%, 33.9%, and 6.7%, respectively, compared with the classical target detection, such as Faster R-CNN, SSD, and YOLOX. The counting trial of rapeseed seedlings demonstrated that the counting model achieved the maximum precision of 96.34% with an average of 93.75%. Furthermore, the rapeseed counting efficiency exceeded the well-trained operator with an average increase of 9.52 times. In the case of online real-time counting of rapeseed seedlings, the maximum difference in counting precision was 1.87% on the UAV counting platform under different weather conditions. The excellent generalization, counting precision, and efficiency fully met the real-time requirements for rapeseed seedlings. The finding can provide a powerful reference to assess the quality of rapeseed seeding and field management. [ABSTRACT FROM AUTHOR]

Agriculture in Taiwan is facing problems such as population aging and labor shortage. Therefore, effectively managing the crops in the planting process is often impossible, easily exposing them to disease threats and leading to heavy losses. This study builds a crop disease detection system and applies it to apple scab, tomato early blight, late blight, and leaf mold. The system first collects apple and tomato crop photos and crop disease data from the Plantvillage dataset on the Kaggle platform. Secondly, according to the crop dataset, the crop type recognition and leaf detection model is trained to identify crop types and detect plant leaves. Finally, the detected leaves are inputted into the disease recognition model for disease detection, and the plants infected with the disease are found. The crop detection system intends to help farmers improve the efficiency of monitoring crop disease through aerial cameras so that farmers can treat plants early and avoid agricultural impacts caused by crop disease. [ABSTRACT FROM AUTHOR]

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Pinewood has been widely applied as the raw material in furniture, construction, and interior decoration, due to its lighter weight, pleasant aroma, and visually appealing texture. The high qualities of pine can greatly contribute to the unique aesthetic of the final products with durability and functionality. One of the pivotal steps is to detect the surface defects in the processing of pine wood. These defects can significantly dominate the appearance and structural integrity of finished goods, even the purchasing decisions and overall usage experience. This study aims to enhance the detection accuracy and efficiency of surface defects for the high quality of pine wood and its derivative products. A pioneering model was introduced to identify such imperfections. The performance and efficiency were then improved in the Dubbed RIC-DETR model using the original RT-DETR. The initial phase of images was acquired from a publicly accessible defects dataset of wood surfaces. A dataset of surface defects was assembled to contain 13 642 labeled images after annotation and data augmentation. Seven types of defects were divided to simulate the potential variations in the lighting conditions, in order to ensure the data generalizability under different scenarios. Subsequently, systematic analysis was conducted with an eye toward finding an optimal balance between accuracy and computational complexity using various network structures, such as VGG, ResNet, and VanillaNet. Ultimately, ResNet18 was selected as the backbone network of feature extraction, due to its efficiency and effectiveness. The basic blocks within ResNet18 were then enhanced using the inverse residual mobile module. A strategic procedure was updated to expand the receptive field and the interaction between layers. The cascaded group attention mechanism was applied to reduce the computational resource consumption with the high expressive capabilities of the EfficientViT model. The RIC-DETR model was deployed with an average precision rate of 97.2%. A better performance was also achieved in total parameters of 15.2 M, floating-point operations of 46.8 G, and a memory footprint of 30.4 MB. Notably, exceptional proficiency was realized to detect seven types of defects with the highest recognition accuracy of 99.3%. Compared with the RT-DETR, the RIC-DETR model improved the average precision by 0.3 percentage points, whereas, the number of parameters, floating-point operations, and memory usage were reduced by 54%, 57%, and 52%, respectively. Moreover, the RIC-DETR improved the average precision by 2.1, 4.6, 1.4, and 0.8 percentage points, respectively, with the best frame rate of 63.5 frames per second, compared with four mainstream models from the YOLO series. Therefore, the RIC-DETR model can be expected to detect the surface defects of pine wood, in terms of detection efficacy, computational rate, and resource utilization. The neck encoding and decoding prediction can be optimized in the further RIC-DETR model. An adaptive scaling strategy can be recommended for the specific task and hardware in the neck encoding network for a better balance between speed and accuracy. The attention mechanisms can also be introduced to construct more effective feature decoding for high accuracy. This finding can provide a solid foundation to detect surface defects of pine wood in the modern processing industry. [ABSTRACT FROM AUTHOR]

Copyright of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective To explore the effect of various input resolution of X-ray images on the performance of the You Only Look Once (YOLO) network in recognition of intertrochanteric fractures. Methods X-ray anteroposterior data of the patients with intertrochanteric fractures admitted in Army Medical Center of PLA from 2017 to 2022 were collected, and finally, 426 patients and 847 images were retained after exclusion criteria. Based on the 2018 guideline of Arbeitsgemeinschaft für Osteosynthesefragen/Orthopaedic Trauma Association (AO/OTA) and actual clinical incidence, these intertrochanteric fractures were reclassified into grades A1.2/A1.3/A2.2/A2.3/A3, and the X-ray images were assigned into training set (678 images), validation set (84 images), and test set (85 images) in a ratio of 8 ∶1 ∶1 in order to maintain strict consistency across each experiment. Eight common resolutions were set as input size for YOLOX-Swin-Transformer, YOLOX, YOLOv5, and YOLOv4 object detection networks. The training set was trained using both training from scratch and transfer learning. The training time was recorded, the test set was used to test the model, and evaluation metrics was recorded. SPSS20.0 statistical software was employed for statistical analysis. Regression analysis was applied to test curve fitting of training time and mean average precision (mAP) values. Frequency statistics function was performed to count the frequencies of evaluation indicators rated as excellent at each input resolution in order to determine the optimal range. Results The image input resolution was positively correlated with the training time of various networks, with all P-values < 0.05, showing statistical significance by linear regression analysis. The quadratic curve fitting of the image input resolution and the mAP mean value of the network resulted in an R2=0.834 (R2>0.5) and P=0.011 (P < 0.05), indicating a good fit of the curve and statistical significance in the regression analysis. When the input image resolution was in a range of 480×480, 576×576, 640×640, the frequency of optimal evaluation index showed the highest, accounting for 42.86%. Conclusion The training time is extended with the increase of resolution. To achieve optimal recognition performance when using YOLO series networks for downstream tasks in medical image recognition, the image input resolution should be controlled within the range of 480×480, 576×576, 640×640, without altering the network architecture.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: This paper aimed to improve the accuracy of identification of small individual betel nuts and the degree of automation of betel nut processing plant by combining with deep learning. Methods: In this study, a novel feature extraction network named Mob-darknet-52 was proposed to construct a method of betel nut location and recognition based on improved YOLO algorithm by using multi-scale detection size. Results: the test showed that the proposed method had a detection accuracy of 94.8%, an accuracy rate of 94.5%, a recall rate of 95.1%, and a detection time of 6.679 ms in betel nut classification. Conclusion: The optimized algorithm based on improved YOLOV3 network can realize the rapid location and identification of betel nut in dense environment.

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, based on the picture collection of tea shoot growth in the field, we used deep learning target detection algorithm YOLOv5 to construct a model for identifying different growth stages of tea shoots, and the testing results indicate that the model had high accuracy. Furthermore, the Image-J software and the image processing methods of threshold cutting based on Gray, RGB and HSV values were applied to process tea leaf area, and the accuracy and efficiency of different methods were compared. The results show that the accuracy of HSV-based algorithm system of cutting tea leaves and automatically calculating tea leaf area was over 94%, which had better performance than RGB-based algorithm system. The research results provide technical support for the intelligent recognition model of tea growth state and information extraction algorithm of leaf traits, and also build a theoretical basis for the development of tea bud automatic recognition module of tea plucking machinery. [ABSTRACT FROM AUTHOR]

For the occlusion problem of pedestrian detection in dense scenes, this study proposes the SC-YOLOv4 crowd detection network based on YOLO. Based on the CSPNet structure of YOLOv4 and combined with the idea of ShuffleNetv2 network, the common convolution structure is improved, and the original common residual module is replaced with the Shuffle Module. A backbone network structure based on S-CSPDarkNet53 is proposed, which preserves the accuracy and reduces the number of network parameters. The centroid prediction module is designed on the basis of retaining the original PANet structure, and the original three output feature layers are replaced with a centroid-based prediction method, that is, the regression and training of the target center point are carried out to calculate the loss, and the original NMS operation is discarded to further improve the detection accuracy in the case of occlusion. The experimental results show that YOLOv4 with S-CSPDarkNet53 structure on the CrowdHuamn data set reduces the amount of parameters and improves the detection speed by 5.2 frame·s-1 when compared with the original network. Compared with YOLOv4, the final SC-YOLOv4 network improves the detection speed by 4.9 frame·s-1. [ABSTRACT FROM AUTHOR]

The YOLO-based algorithm is one of the hot research directions in target detection. In recent years, with the continuous proposition of YOLO series algorithms and their improved models, the YOLO-based algorithm has achieved excellent results in the field of target detection and has been widely used in various fields in reality. This article first introduces the typical datasets and evaluation index for target detection and reviews the overall YOLO framework and the development of the target detection algorithm of YOLOv1~YOLOv7. Then, models and their performance are summarized across eight improvement directions, such as data augmentation, lightweight network construction, and IOU loss optimization, at the three stages of input, feature extraction, and prediction. Afterwards, the application fields of YOLO algorithm are introduced. Finally, combined with the actual problems of target detection, it summarizes and prospects the development direction of the YOLO-based algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of detecting surface defects on industrial parts, a target detection method based on improved YOLOv3 is proposed. The latest attention mechanism SA (Shuffle Attention) with channel shuffling is introduced and combined with the residual unit of the Darknet-53 backbone structure of the YOLOv3 model to form the SA residual block structure, which fully utilizes the feature channel information to obtain the YOLOv3-SA model. For different datasets, the input images are scaled at different scales, and the K-means method is used to cluster the real bounding boxes to improve detection efficiency. The experimental results show that the recall rate of the YOLOv3-SA model reaches 95.4%, and the mAP can be increased by up to 7 % compared to YOLOv3. [ABSTRACT FROM AUTHOR]

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With the development of aerial refueling technology, autonomous aerial refueling(AAR) has become an important technology in the future battlefield, which is a promising prospective and challenging topic. Since the relative position between the receiver and the drogue is important to accomplish the AAR task, a neural network-based image recognition method is proposed to acquire the required information. Firstly, a C language-based YOLO network is used as the initial network, which meets the requirements of the on-board VxWorks system and can be run directly on the hardware. Then, considering the physical characterizes of the drogue, a multi-dimensional anchor box is designed and the network structure is optimized to adapt to the multi-dimensional situations. Finally, to address the problem of results shifts, feature maps with various sizes and the optimized loss function are used to optimize the network, where the pyramid structure suggests the design of feature maps. The experimental results indicate that the presented method can recognize the drogue more accurately and quickly.

Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For synthetic aperture radar (SAR) multi-target detection applications, this paper proposes an anchor free SAR image ship target detection method based on the you only look once (YOLO) framework. This method is aimed at the disadvantage that the YOLOv3 anchor needs to be preset and cannot fit perfectly. By adopting the anchor free method, it can better adapt to the size of the detected target and facilitate the use of multi-scale targets. On this basis, the attention mechanism is added to the CSPDarknet53 network as a feature extraction network, and then alter an improved feature pyramid network (FPN) that can increases the receptive field, the feature map is transmitted to the anchor free detection head, which effectively improves the prediction precision of the target category and location. Experiments show that the improved algorithm has an average precision of 3. 8 % higher than YOLOv3 on the public SAR ship data set, reaching 94. 8%, and the false alarm rate is reduced by 4. 8%. [ABSTRACT FROM AUTHOR]

In order to solve the clean boat problem of detecting and tracking the small volume or occlusion of floater in the background of complex water surface, a target detection and tracking method under the background of surface based on visual algorithm was proposed. YOLO improved multiple granularity characteristics fusion method was used to make the model in the final test when the extracted feature vector to consider more features of the underlying. And K neighborhood search interested area module was introduced. The module combined with both short-term and long-short term memory(LSTM) made up the temporal correlation of the convolutional neural network with poor defects, according to the target current frame semantic features and motion prediction target location in the next frame, which makes the feature extraction of the target faster and effectively removes the interference of complex background. The results show that the average success rate, average accuracy and average speed of the algorithm are 57. 1%, 71. 1% and 45. 4 frame/ s, respectively. It is concluded that the algorithm can better solve the problem that the detected target is too small and improve the tracking performance when the tracking target is obscured. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In coastal surveillance video images of ship target recognition problem, the speed and accuracy of traditional detection methods need to be further improved. The deep learning method has been able to recognize ship targets, but the model network structure is complex and there are interaction effects of hyper parameters, the model training time is long, and the identification accuracy is often limited by the selection of training samples and hyper parameters. In the course of convolutional neural networks(CNN) model training of you only look once(YOLO) algorithm, the concept of uniform design was planned to be adopted to coordinate optimization of the level values of multiple influencing factors, so as to reduce the blindness of adjusting the parameters of deep learning algorithm according to subjective experience. After analyzing the impact of clarity and manual annotation, the scene, sample ratio of training set, training algorithm and training rounds were taken as the influencing factor. Differentiated level values were set and the deep learning model was trained by uniform design method. Using the value of average precision(AP) to compare and analyze the empirical method and uniform design method. Taking three water areas of Huangpu River in Shanghai as example, the results show that among the 9 groups of schemes designed by the general empirical method, the optimal value of AP is 0.84. Among the 6 group of schemes generated by the uniform design method, when YOLOv5x algorithm is selected, the sample proportion is 90% and 100 training rounds, the accuracy of deep learning model is high, and the optimal AP value is 0.91, It is concluded that the effectiveness of the uniform design method in hyper parameter training of deep learning model. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of multi-directional and multi-angle text extraction and recognition in Chinese traffic guidance signs, a light-weight Chinese traffic guidance sign text extraction and recognition algorithm is proposed that integrates convolutional neural networks and traditional machine learning methods. Firstly, the YOLOv5l object detection network is lightly improved, and the YOLOv5t network is proposed to extract the text regions in the road signs. Secondly, an M-split algorithm combining the projection histogram method and the polynomial fitting method is proposed to segment the extracted text regions. Finally, the MobileNetV3 lightweight network is used to recognize the text. The proposed algorithm achieves a close-shot text recognition accuracy of 90.1% on the self-made TS-Detect dataset, the detection speed achieves 40 fps, and the size of the weight file is only 24.45 MB. The experimental results show that the algorithm is lightweight and accurate enough to complete the real-time Chinese guide sign text extraction and recognition tasks under complex shooting conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian Polytechnic University is the property of Journal of Dalian Polytechnic University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

SAR image target recognition mainly aims at strategic military targets such as bridges and airports, as well as tactical targets such as aircraft, tanks, and automobiles. Accurate identification, classification and positioning is an important part of SAR image interpretation. Firstly, the main processing layer of the convolutional neural network based on C6678 is constructed. Secondly, the processing and storage characteristics of C6678 are combined to optimize the design of the convolutional layer and network scheduling. Finally, a design and implementation method of the YOLOv3-TINY target recognition network on C6678 is completed. This method can reconstruct and modify common convolutional neural network models, and solves the problem of running deep learning networks on multi-core processing platforms such as C6678. The experimental results show that the method is consistent with GPU in detection performance. Considering the real-time image frame rate of airborne SAR, although the real-time performance of this method on C6678 is far from that of GPU, it can meet the real-time processing requirements of airborne SAR. [ABSTRACT FROM AUTHOR]

Since the camouflage object is highly similar to the surrounding environment with a rather small size,the general detection algorithm is not fully applicable to the camouflaged object detection task,which makes the detection of camouflaged object more challenging than the general detection task.In order to solve this problem,the existing methods are analyzed in this paper and a detection algorithm for camouflage object is proposed based on the YOLO v5 algorithm.A new feature extraction network combined with attention mechanism is designed to highlight the feature information of the camouflage target.The original path aggregation network is improved so that the high,middle and lowly level feature map information is fully fused.The semantic information of the target is strengthened by nonlinear pool module,and the detection feature map size is increased to improve the detection recall rate of the small size target.On a public camouflage target dataset,the proposed algorithm is tested with 7 algorithms.The mAP of the proposed algorithm is 4.4% higher than that of the original algorithm,while the recall rate has improved 2.8%,which verifies the effectiveness of the algorithm for camouflaged object detection and the great advantage in accuracy compared with other algorithms.

The identification of microfossils in carbonate rocks with thin-section observation is of great significance for the study of sedimentary environment, but the traditional method by manual identification is highly experience required and is greatly affected by subjective factors.In this paper, a method for microscopic recognition of carbonate rocks based on ResNet convolutional neural network was introduced. Through image preprocessing, model design, model training etc., the intelligent recognition of fossils of organisms within thin section images were realized, and the recognition accuracy showed to be 86%.Meanwhile, an advanced YOLO(You Look Only Once) object detection model was proposed, which could realize the detection and recognition of the area where the organism locates in thin section image, and the recognition accuracy appeared to be 85%.This method verified the feasibility of using digital image processing algorithm and deep learning method to intelligently identify biological microscopic images of carbonate rocks.It can be regarded as a useful supplement to traditional manual identification methods and has certain practical application value.

In order to solve the problems of low accuracy, wrong detection and missing detection of traditional target detection algorithms in blood cell detection task, a target detection algorithm YOLO-Att based on improved YOLO framework is proposed. Based on the YOLO framework, a multi-scale residual enhancement module is added to the backbone network to improve the utilization rate of feature information by combining with the feature level of low-level informationrich network. An attentional gating structure embedding model is designed to obtain more high quality information of main features. At the same time, Focal loss is used to replace the cross entropy in the original loss function to improve the weight of positive and negative samples and accelerate the convergence rate of the model. Finally, K -means++ clustering algorithm is used to optimize the anchor frame of the target to further improve the detection accuracy. Compared with the existing target detection algorithms such as Faster-RCNN, SSD and YOLOv4, YOLO-Att increased mAP to 66.32% in the BCCD detection task of the universal blood cell data set, and the detection rate reaches 85.4 ms, which is more in line with the real-time detection task of blood cell. [ABSTRACT FROM AUTHOR]

Tomato has been one of the most widely cultivated greenhouse crops. Autonomous picking vehicles have been highly demanding in recent years, due to the labor shortage for the hand-picking under the population aging and the harsh working environment for tomato harvesting. Among them, the real-time recognition of tomato maturity can be one of the key technologies for autonomous tomato picking vehicles. However, current advanced target recognition can be confined to the low accuracies for real-time purposes, when detecting the tomatoes with severe occlusions or small sizes. In this study, a tomato maturity recognition was proposed using an improved YOLOv4-tiny. Firstly, a detection head with 76×76 size was added to the head network to improve the recognition performance of small-sized tomatoes using the feature maps in deeper layers. Then, Convolution Block Attention Module (CBAM) was integrated into the backbone network of YOLOv4-tiny. This module was focused on the significant characteristics across the channel- and spatial-wise dimensions, thereby improving the recognition performance of occluded tomatoes. Next, the feature extracting accuracy of the ReLU activation function decreased significantly, as the convolutional layer deepened in the YOLOv4-tiny. The ReLU was replaced by the Mish activation function to maintain the accuracy in the backbone network of YOLOv4-tiny, whose accuracy slightly decreased with the deep layers. Finally, Densely Connected Convolution Networks (DCCN) were adopted to enhance global feature fusion, in order to alleviate the loss of features and the vanishing of gradients in the convolutional process. As such, the feature propagation was strengthened for feature reuse, thereby substantially reducing the number of parameters. An image dataset was also built to detect the maturity of cherry tomatoes, where 1300 pictures of greenhouse tomatoes in the size of 1 280×720 covered a variety of complex scenes of severe occlusions, changeable lighting conditions, and various fruit sizes. All tomatoes were labelled by either ripe or underripe ground truth bounding boxes using the image annotation tool LabelImg. All images were divided into the training and test set with a ratio of 8:2 using the holdout method, where one-fifth of the test set was used as a validation set for the training. All models were iterated 300 times, and the learning rates were attenuated by 10% every time at 50%, 80%, and 90% of the total iterations, respectively. The improved YOLOv4-tiny-X model was compared with YOLOv3, YOLOv4, YOLOv5m, and YOLOv5l. Both the Intersection over Union (IoU) and non maximum suppression (NMS) thresholds were set to 0.5. Experimental results show that the Mean Average Precision (mAP) of the improved model was 97.9%, which was 30.9, 0.2, 5.4, and 4.9 percentage points higher than the rest models, respectively. The improved model run at a rate of 111 frames per second on the Nvidia GTX 2060 GPU. Precision, Recall, F1 Score, and mAP50 of the improved YOLOv4-tiny model increased by 0.4, 3.4, 2, and 0.7 percentage points, respectively, compared with the original. The weight file of the improved model was less than 1/6 in size, the twice faster detection speed, the 339 less layer number of the model, and the computational cost of 20.2% in GFLOPs, compared with the YOLOv4. In conclusion, the YOLOv4-tiny-X presented the highest target recognition accuracy, and an extremely high detection speed, compared with the advanced target recognition models. Consequently, the improved model can be expected to identify the occluded and small-sized tomatoes with the high recognition accuracy and the better performance in a complex picking environment. The finding can provide a strong reference for the real-time recognition systems of tomato maturity in autonomous tomato picking vehicles [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An accurate recognition of fruit and vegetable depends mainly on the occlusion of vine, leaf, and light during robotic harvesting at present. In this study, a feasible recognition algorithm was proposed to efficiently identify the ripe tomatoes in the natural environment using YOLO v4 and HSV. The data set of mature tomatoes was also collected to capture some obscure images with the vines and leaves or color-changing by light under the complex growth environment. Once the original YOLO v4 network was utilized to identify the tomatoes after learning these samples, some tomatoes in the green ripening and color transition stage were taken like in the mature stage. Therefore, an HSV processing was added into the detection box of the original YOLO v4 network, in order to segment the red region of tomatoes. The specific tomatoes were taken as the target output to improve the accuracy of recognition if the red areas of segmentation reached a critical proportion in the detection box. The size of the proportion presented an important impact on the accuracy of recognition. The recognition performance was also compared on the test set under different proportions. As such, the proportion of 16% was taken as the tomato recognition at the mature stage. At this time, the highest recognition accuracy of the combined YOLO v4 and HSV was 94.77%, 4.30 percentage point higher than that of the original. The detection speed of a single image in the workstation was 25.86 ms. It indicated that the addition of HSV processing was widely expected to improve the accuracy of the original network. Furthermore, the improved network was also used to effectively remove immature tomatoes that cannot be recognized by the improved Cascade RCNN. In addition, the running time was tested ranging from the calling RealSense D435i to the first target tomato on the workstation and the miniature industrial computer. It was found that the average time of recognition was 0.51 s on the workstation, and 1.48 s on the miniature industrial computer, using the combined YOLO v4 and HSV from turning on the camera to the first target detection. Consequently, the improved algorithm was fully met the real-time requirements of mechanical picking. This finding can also provide a strong theoretical basis for the accurate, efficient, and real-time recognition of fruit and vegetable picking using robots in a complex environment. [ABSTRACT FROM AUTHOR]

Small and hidden conditions of dense cherry tomatoes have posed a great challenge on the rapid identification and positioning of fruits. A new key technology with a strong robustness is highly demanding to improve the efficiency and yield prediction of cherry tomatoes in the facility agriculture environment. In this study, a novel recognition method was proposed to locate the dense cherry tomatoes using improved YOLOv4-LITE lightweight neutral network. A mobileNet-v3 easy migration to mobile terminals was selected as the feature extraction network of model to construct a YOLOv4-LITE for a higher detection speed of cherry tomatoes. A feature pyramid network was set as the modified (FPN) + Path Aggregation Network (PANet) structure, in order to avoid replacing the backbone network to reduce the detection accuracy. Specifically, a 104×104 Future map was introduced to achieve fine-grained detection for the small targets. More importantly, a deep separable convolution was used in the PANet structure to reduce the amount of model calculations. The new network was more lightweight, where the generalization ability of model was improved by loading pre-training weights and freezing partial layer training. A comparison was made on the recognition effect of YOLOv4, F1 and AP on the test set with the same degree of occlusion or adhesion, further to evaluate the difference between the models. The test results show that the improved FPN structure on the basis of YOLOv4 was higher than the AP50 of the original YOLOv4 AP75 increased by 15 percentage points, and the F1 increased by 0.14 and 0.24 under the corresponding IOU threshold. However, the weight increased by 4MB, while the detection speed increased by 5.84%, and the amount of network parameters increased by 14.85%. The improved FPN structure on the basis of YOLOv4+MobiletNet-V3, AP50 increased by 0.14%, AP75 increased by 23.25 percentage points, F1 value increased by 0.14 and 0.49 under the corresponding IOU threshold, indicating that YOLOv4 and YOLOv4+MobiletNet-V3 lacked small goals. Fortunately, the Future map of small targets was added to improve the fine-grained detection of model, but the amount of model parameters and weights increased accordingly. As such, the PANet structure was improved to introduce a deep separable convolutional network, while ensuring high F1, AP, Recall and Precision. An optimal performance was achieved, where the model weight was compressed to 45.3MB, the detection speed was 3.01ms/sheet, and the network parameters were 12026685. Specifically, the new network was reduced by 198.7MB, increased by 34.85%, and reduced by 81.34%, respectively, compared with the original YOLOv4. It was also reduced by 55.9%, slower by 0.23 s, and reduced by 69.97%, respectively, compared with YOLOv4+MobiletNet-V3. The data indicated that the improved PANet strategy presented the similar accuracy under such circumstances, while effectively reduced memory consumption, and the amount of model parameters, but accelerated the speed of model recognition. The F1, AP50, and recall of the proposed recognition model for the dense cherry tree on all test sets were 0.99, 99.74% and 99.15%, respectively. The improved YOLOv4 increased by 0.15, 8.29, and 6.54 percentage points, respectively, and the weight size was 45.3MB, about 1/5 of YOLOv4. Additionally, the detection of a single 416×416 image reached a speed of 3.01ms/frame on the GPU. Therefore, the recognition model of dense cherry tomatoes behaved a higher speed of recognition, a higher accuracy, and lighter weight than before. The finding can provide a strong support to the efficient production forecast of cherry tomatoes in the facility agriculture environment. [ABSTRACT FROM AUTHOR]

Pine wilt disease is a fast-spreading and destructive forest disease which can damage the entire pine forest in a short time. The key to control the disease is to identify abnormally discolored trees in a quick and accurate way. In this study, a fixed-wing Unmanned Aerial Vehicle (UAV)equipped with a professional true-color camera was used for image acquisition, and a deep learning algorithm—YOLO (You Only Look Once), was adopted to detect the images of abnormally discolored tree. The test was conducted on the platform using a RTX 2080 GPU with 8GB memory in the same parameters. The study area was divided into one training area and two testing areas, of which the training area and the testing area 1 were located in Xinjing Mountain, and the testing area 2 was located in Baota Mountain. In order to improve the efficiency and accuracy of the algorithm, depthwise separable convolution and inverted residual block were employed to improve YOLO algorithm, and the upward transmission of the low-level features of location of the Neck was removed. In order to verify the performance of the improved algorithm, the improved algorithm was compared with Faster R-CNN, EfficientDet, YOLOV4 and YOLOv5 in terms of accuracy precision, training time of per epoch, size of parameters and testing time of per image. To be more Specifically, the accuracy precision of the improved YOLO algorithm increased to 80.85%, which were 1.25%, 3.02%和 3.49% higher than Faster R-CNN, YOLOv4 and YOLOv5 respectively,and was 0.42% lower than EfficientDet; the training time of per epoch of the improved algorithm was 164s, which was 187, 172 and 115 s shorter than Faster R-CNN, EfficientDet and YOLOv4 respectively, and was 26s longer than YOLOv5; the parameters size of the improved YOLO algorithm registered 44.23 MB, which was 477.35 and 199.69 MB smaller than Faster R-CNN and YOLOv4,and was 29.62 and 17.27 MB bigger than EfficientDet and YOLOv5; the testing time of per image of the improved YOLO algorithm decreased to 17ms, which was 68, 33 and 7ms less than Faster R-CNN, EfficientDet and YOLOV4 and was 9 ms longer than YOLOv5. Therefore, comprehensive evaluation of the four indexes showed that the improved YOLO algorithm performed better. In order to verify the applicability of the same model in different areas, the model built on images of training area of Xijing Mountain was employed to detect images in both testing areas of Xijing Mountain and Baota Mountain. The results showed that in Xijing Mountain, the improved YOLO algorithm correctly detected 582 trees, wrongly detected 109 trees and missed 51 trees among 633 abnormally discolored trees by pine wilt disease in total. The precision, recall and F1 reached 84.23%,91.94% and 87.92% separately. In Baota Mountain, the same algorithm successfully detected 708 trees, incorrectly detected 109 trees and missed 157 trees in a total of 865 object trees. The precision, recall and F1 were 86.66%,81.85% and 84.18% in separate. Although the F1 of Baota Mountain was slightly lower than that of Xijing Mountain, it basically met the detection requirements of abnormally discolored trees caused by pine wilt disease. The F1 of two testing areas showed that the model possessed certain adaptability and meant the same model can be used to identify target trees in different areas, which can reduce the modeling time and improve the efficiency of recognition. Through the results of the recognized images, it could be found that the similar ground features, canopy density, slope aspect and topography were the main factors that had an influence on the accuracy of detection. In automatic detection, the similar ground features would lead to acquire features similar to abnormally discolored trees and the canopy density, slope aspect and topography would acquire insufficient feature, which would easily lead to misjudgment and missed detection of abnormally discolored trees. Although these factors would affect the accuracy of detection, the impact is relatively small. In conclusion, the improved YOLO algorithm can automatically identify trees attracted by pine wilt disease in a more accurate and efficient manner, which was conducive to monitoring of pine wood disease rapidly, knowing the incidence timely and providing objective data for the prevention and control of pine wilt disease. [ABSTRACT FROM AUTHOR]

In this paper, an obstacle detection method based on YOLOV3 algorithm is proposed for obstacle detection of medium-low speed trains in transit. First, the deep learning algorithm is used to eff ectively identify obstacles in the scene. Then, the edge detection algorithm based on Freeman chain code is used to extract the edge of the train track, so as to determine whether the obstacles aff ect the traffi c and give a warning to the driver. At the same time, the YOLOV3 network data set is expanded by means of transfer learning, so as to achieve the purpose of improving the target obstacle recognition accuracy by use of this method in a specifi c scene. The experimental results show that this method has high applicability and is a convenient and effi cient obstacle detection method for trains in transit. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)