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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.)

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 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.)

Aiming at the YOLOv4 model's difficulty in dealing with occluded pedestrians in real scenarios, this paper made improvements in ensuring the real-time performance of the YOLOv4 model and applied the YOLOv4 model to pedestrian detection. In order to improve the model's ability to detect occluded pedestrians, the model adopted the K-means ++ clustering algorithm to re-design the priori frames applicable to the pedestrian target sizes, and introduced the exclusion loss function term to maximise the distance between the candidate frames and the neighbouring real frames of non-mate-hing targets, and minimise the overlap ratio between the candidate frames and the real frames of other targets. The improved model was experimented on the challenging datasets CrowdHuman and Caltech, and the experimental results verified the effectiveness of it. Finally the model has been applied to video pedestrian detection in real scenarios, which also verified the effectiveness of the improvements in this paper. [ABSTRACT FROM AUTHOR]

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.)

To eliminate the defects of incomplete detection and high false detection rate caused by insignificant pedestrian target features, dense small targets and complex background in infrared images, this paper proposes an infrared pedestrian target detection algorithm based on improved YOLOv7. Firstly, the original Spatial Pyramid Pooling (SPP) module is replaced by the Channel Attention based Spatial Pyramid Pooling (CASPP) module based on the YOLOv7-tiny model, so that the model could pay more attention to the extraction of pedestrian features; then, the convolution module CBM based on the Meta-ACON activation function is introduced, which further suppressed the background noise and preserved the details of the pedestrians; finally, an alpha fusion data enhancement method is proposed to enrich the diversity of samples and improve the stability of the model in complex environments. The validation based on the FLIR dataset shows that the proposed method improves the accuracy by 3% and reduces the computation by 38% compared with the YOLOv7-tiny algorithm, which is more suitable for infrared pedestrian target detection scenarios. [ABSTRACT FROM AUTHOR]

In response to the complex and diverse nature of the road traffic environment, where vehicle and pedestrian detection is prone to false and missed detections, this paper proposes a vehicle and pedestrian target detection algorithm YOLOv8-RC based on multi-scale feature fusion. Initially, the RCS-OSA module is introduced within the structure of the base network YOLOv8 to replace the original module, thereby enhancing and integrating the extracted feature information. Additionally, a lightweight Context-Aware Adaptive Feature Reorganization (CARAFE) is employed to replace the original upsampling operator, enhancing the network s capability for global multi-scale information fusion. Subsequently, a detection dataset consisting of 6 000 images of vehicle and pedestrian targets is constructed through public datasets and network collection. The algorithm s detection performance is quantitatively evaluated using accuracy, recall rate, mean Average Precision at a 50% intersection over union threshold (mAP50), and mAP50-95. Compared to YOLOv8-N, YOLOv8-RC demonstrates an improvement of 1.7 percentage in accuracy, 1.2 percentage in recall rate, 0.9 percentage in mAP50, and 0.5 percentage in mAP50-95, thus validating the algorithm's effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou 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.)

Aiming at the high false negative rate and low accuracy in crowded scenes, a pedestrian detection network based on multi-scale features and mutual supervision is proposed. To effectively extract pedestrian feature information in complex scenes, a network combining PANet pyramid network and mixed dilated convolutions is used to extract feature information. Then, a mutual supervision detection network for head-body detection is designed, which utilizes the mutual supervision of head bounding boxes and full-body bounding boxes to obtain more accurate pedestrian detection results. The proposed network achieves 13.5% MR-2 performance on CrowdHuman dataset, with an improvement of 3.6% compared to the YOLOv5 network, and a simultaneous improvement of 3.5% in average precision (AP). On CityPersons dataset, it achieves 48.2% MR-2 performance, with 2.3% improvement compared to the YOLOv5 network, and a simultaneous improvement of 2.8% in AP. The results indicate that the proposed network demonstrates good detection performance in densely crowded scenes. [ABSTRACT FROM AUTHOR]

To provide drivers with real-time and accurate pedestrian information and reduce traffic accidents, the detection of enhanced YOLOv3-tiny (DOEYT) pedestrian detection algorithm was proposed. The robust feature extraction network was established, and the asymmetric max-pooling was used for down sampling to prevent the loss of lateral pedestrian features due to the increased receptive field. Hardswish was employed as activation function for the convolutional layers to optimize network performance, and the global context (GC) self-attention mechanism was used to capture holistic feature information. In the classification and regression network, the three-scale detection strategy was adopted to improve the accuracy of small-scale pedestrian target detection. The k-means++ algorithm was used to regenerate dataset anchor boxes for enhancing network convergence speed. The pedestrian detection dataset was constructed and divided into training and testing sets to evaluate DOEYT performance. The results show that by the asymmetric max-pooling, Hardswish function and GC self-attention mechanism, AP values are increased by 14.4%, 7.9% and 10.8%, respectively. On the testing set, DOEYT achieves average precision of 91.2% and detection speed of 103 frames per second, which demonstrates that the proposed algorithm can quickly and accurately detect pedestrians for reducing the risk of traffic accidents. [ABSTRACT FROM AUTHOR]

In largescale crowded places, abnormal crowd gathering occurs from time to time, which brings certain challenges to the dense pedestrian detection technology involved in application scenarios such as autonomous driving and largescale public place crowd monitoring systems. The new generation of dense pedestrian detection technology requires higher accuracy, smaller computing overhead, faster detection speed and more convenient deployment. In view of the above requirements, a lightweight dense pedestrian detection algorithm MER-YOLO based on YOLOv8 is proposed, which first uses Mobile ViT as the backbone network to improve the overall feature extraction ability of the model in pedestrian gathering areas. The EMA attention mechanism module is introduced to encode the global information, further aggregate pixel level features through dimensional interaction, and strengthen the detection ability of small targets by combining the detection head with 160×160 scale. The use of Repulsion Loss as the bounding box loss function reduces the missed detection and misdetection of small target pedestrians under dense crowds. The experimental results show that compared with YOLOv8n, the mAP@0. 5 of the MERYOLO pedestrian detection algorithm is improved by 4. 5%on the Crowd Human dataset and 2. 1%on the Wider Person dataset, while only 3. 1x106 parameters and 9. 8 GFLOPs, which meet the deployment requirements of low computing power and high precision. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui 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 China Sciencepaper is the property of China Sciencepaper 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.)

In the driving scenario, due to the occlusion between pedestrians and their scale variations, detection model have low accuracy, high model parameters, and difficulty in deploying to mobile terminals. This paper proposes a lightweight real-time pedestrian detection model, LPD-YOLO, based on the YOLOv5s model. Firstly, in the feature extraction part, the original backbone network is replaced with MES Net (Mish-Enhanced Shuffle Net), and an attention module SA (Shuffle Attention) is embedded in the backbone network to enhance network feature extraction ability. Secondly, in the feature fusion part, the original PANet is improved by using the DS-ASFF structure to fully fuse feature maps of different sizes. Then, standard convolution is replaced with GS convolution in the feature-covergent network part without affecting accuracy, further reducing model parameters and computation. Finally, in the prediction part, the original loss function is improved by using the OTA label assignment strategy combined with α-IOU to accelerate model convergence. Experimental data show that compared with YOLOv5s, LPD-YOLO has 81.2% fewer parameters, 46.3% lower floating-point operation volume, 75.8% smaller model size, and 3.3% higher detection accuracy. The single image detection speed is 13.2 ms, which better meets the real-time detection requirements of dense pedestrians in driving scenarios. [ABSTRACT FROM AUTHOR]

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.)

Pedestrian object detection and tracking have attracted much attention in the computer vision field. An improved multi-pedestrian tracking model is proposed, which improves the basic framework of Deep SORT and integrates Mask R-CNN to realize the detection, tracking and pose estimation f pedestrian. The anchor boxes with the more suitable aspect ratio for pedestrian target are adopted, which replace the anchor boxes of RPN to speed up the model and improve performance without complex calculation. In addition, attention mechanism is introduced into the deep residual network, i. e., the lightweight SKNet is used to choose the best convolution kernel adaptively to improve the feature representation for target detection. The histogram of gradient feature combined with color information is adopted instead of the convolution feature, which improves appearance feature association matching in the Deep SORT model so as to track pedestrian targets effectively under occlusion. The impact of various improvements on the model are verified through ablation studies, and the proposed model is compared with the current mainstream model. Experimental results show that the improved models are effective, which improves MOTA of NSH by 6% on the MOT16 tracking data set. The test performance of our proposal on the public datasets is superior to that of the compared models. The proposed model can still rack pedestrian targets effectively when the background moves or pedestrian targets are occluded. [ABSTRACT FROM AUTHOR]

Nowadays, the detection model based on YOLOv5 network are widely used in the task of pedestrian detection, and have good results in terms of accuracy and speed. However, it is often limited by computing power on terminal devices. Therefore, based on the improved backbone network of the RepVGG model, in order to improve the adaptability in dense crowds and complex environments, a coordinate attention mechanism was introduced to expand the receptive field and enhance the weight of the region of interest. After experimental testing, this lightweight network parameter and calculation amount is relatively small, and the detection accuracy and robustness are relatively high, which can meet the requirements of engineering applications to a certain extent. [ABSTRACT FROM AUTHOR]

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 Automotive Digest is the property of Automotive Digest 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 Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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.)

When pedestrian detection is carried out in image and video sequences, there are some problems, such as diverse pedestrian posture and scale as well as pedestrian occlusion, which leads to inaccurate detection of some pedestrians by YOLOv4 algorithm and false detection and missed detection. In order to solve this problem, a complex pedestrian detection model based on improved YOLOv4 algorithm is proposed. Firstly, the ground truth size of pedestrian dataset is analyzed by the improved kmeans clustering algorithm, and the size of anchor box is determined according to the clustering results. Secondly, PANet is used for multi-scale feature fusion to make it more sensitive to multi-attitude and multi-scale pedestrian targets and improve the detection effect. Finally, for pedestrian occlusion problem, the repulsion loss function is proposed to make the predicted box as close to the correct target as possible. The experimental results show that the new de-tection model has better detection effect than YOLOv4 and other pedestrian detection model. [ABSTRACT FROM AUTHOR]

Pedestrian detection in occluded scenes has always been a thorny problem in computer vision. In this case, due to the large difference in scale of occluded pedestrians and low visibility, it usually brings great challenges to detection. To solve this problem, this paper proposes a model structure for pedestrian occlusion detection, which improves the pedestrian detection method based on anchor-free. First, a structure for extracting multi-scale context information is designed. By cascading multiple convolutional layers with different dilation rates, using dense connections to achieve multi-scale feature sharing, the context information of each region is extracted to solve the occlusion problem. In addition, in order to improve the robustness of features, the multi-scale feature fusion is adaptive adjusted using the channel attention mechanism. Experimental results show that this method achieves 41.73% of MR-2 on the occlusion subset of Caltech pedestrian dataset, which is better than other contrast detectors. [ABSTRACT FROM AUTHOR]

To improve the detection precision in actual application scenes, a high resolution feature extraction network High-Resolution Representation Network(HRNet)and a Guided Anchoring mechanism were proposed to improve the RetinaNet algorithm, the high resolution information of the feature maps were maintained during feature extraction and the anchors in network were generated adaptively, which improved the detection precision of the algorithm. The results show that the improved algorithm achieves a mean average precision of 0. 905 on Caltech pedestrian dataset, which is 6. 0% higher than the standard RetinaNet algorithm. The detection speed reaches 19 FPS on video with the image size of 1 280 × 720 pixels per frame, the algorithm achieves the balance between the detection precision and the detection speed. [ABSTRACT FROM AUTHOR]

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 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 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 Heilongjiang University of Science & Technology is the property of Journal of Heilongjiang University of Science & Technology 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 Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University 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.)

To improve the detection performance on occluded and small pedestrians in images， a pedestrian detection method based on semantic segmentation attention and visible region prediction was proposed. Specifically， based on the single shot multi-box detector(SSD)object detection network， the hyperparameter setting of the SSD was firstly optimized to make it more suitable for pedestrian detection. Then the semantic segmentation attention branch was introduced into the network to enhance the pedestrian detection features learned by the network. Finally， a detection prediction module which can simultaneously detect the full bodies and visible regions of pedestrians was developed. This module has the advantage of leveraging the features learned from visible regions to guide the learning of the full-body detection features， hence improving the overall detection accuracy. The experiment carried out on the Caltech pedestrian detection benchmark shows that the log-average miss rate of the proposed method is 5.5%， which is competitive compared with existing pedestrian detection approaches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

Pedestrian detectors based on convolutional neural networks generally adopt image recognition network, which usually causes the following problems:1) multi-pool layers lead to the loss of feature information of small target pedestrian; 2) the single pool method leads to the weakening or even loss of the local important feature information of pedestrians. Therefore, based on the maximum pooling and average pooling methods, an adaptive pooling method is proposed, and combined with the Faster R-CNN, an effective pedestrian detector is formed, so as to enhance the local important feature information of pedestrians and retain the effective feature information of small target pedestrians. Through a large number of experiments on several public pedestrian datasets, the results show that compared with the traditional convolutional neural network pedestrian detector, the proposed method reduces the miss rate by about 2%~3%, which verifies the effectiveness of the method.

Aiming at the problem of single detection function,higher cost and lower detection efficiency and reliability for current pedestrian detection,which was mostly realized by vehicle mounted equipment,a pedestrian detection technology based on smart road was proposed.By deploying a large number of low-cost,highly reliable Internet of things devices on the road,real-time detection of pedestrian information in the surrounding environment was realized.Early warning information can be provided to vehicles with very low latency wireless communication technology,which can improve road safety.At present,the prototype of the pedestrian detection system has been developed,and verified by field test,the pedestrian detection system can detect pedestrians effectively.Within the detection range of 4 meters,the accuracy of single device can reach 80%,and the accuracy of multiple cross-deployed devices can reach 100%.

As a typical problem of object detection, pedestrian detection has been an active research topic in recent years. Pedestrian detection is widely used in intelligent transportation, autonomous driving, video surveillance, behavior analysis and other fields, but there are many problems to be solved. In this study, a multi - channel feature model based on multiple feature fusion which consists of a non - deep learning branch, a body branch and a limb branch is proposed. A small number of high - quality pedestrian candidate areas are extracted through the non - deep learning branch, thus reducing the computational cost cause by exhaustive search by sliding window and improving the computational efficiency. The body branch and limb branch obtained from the feature of multi - layer convolutional channel are applied to detect pedestrians through the overall human body information and the semantic information of human body parts, respectively. Caltech pedestrian dataset and INRIA pedestrian dataset are adopted to train and Lest the proposed model. Experimental results show that combine with the output of each branch, the proposed pedestrian detector have a lower miss rale. The miss rales on INRIA pedestrian dataset and Caltech pedestrian dataset are 8. 24% and 19. 78%, respectively. [ABSTRACT FROM AUTHOR]

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 Donghua University (Natural Science Edition) is the property of Journal of Donghua University (Natural Science) 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.)

Environmental visual perception technology is one of the main means for intelligent vehicles to obtain external environmental information and the precondition for autonomous driving of intelligent vehicles. Based on the comprehensive analysis of the detection technology and methods targeted at different detection objects, the main contents of environmental visual perception research are provided, and the key technologies that the study involves including lane detection technology, vehicle detection technology, the pedestrian detection technology, and traffic signs detection technology are discussed. The advantages and disadvantages of each sub-technique of each technology are analyzed. Finally, on the basis of summarization of the paper, the prospect of unmanned environment of visual perception technology in the future development direction is given:the development of the multi-feature fusion based environment visual perception technology; the fusion application of several environment perception technologies under complicated conditions; the application of the three-dimensional image in picture processing; the application of the improved CNN method in picture processing.

In view of potential safety hazards of accidentally injuring pedestrians during operation process of conveyor at the back of continuous shearer, a pedestrian monitoring and protection system of continuous shearer based on infrared thermal imaging was designed. Firstly, long-wave infrared probe was used to monitor sensitive areas of the conveyor at the back of continuous shearer and real-timely collect infrared image information in the sensitive areas. Then, microprocessor was used to process the infrared image information, and pedestrians were detected; when pedestrians were detected entering the sensitive area, the pedestrian information would be rapidly judged by microprocessor within 1 s, meanwhile an early warning protection strategy would be triggered; when the pedestrians exited the sensitive area, the early warning protection mechanism would stop automatically; when the pedestrians passed over the sensitive area, at that time, an emergency stop control strategy would be triggered by microprocessor, so as to make the conveyor at the back of continuous shearer stop urgently to protect safety of crossing pedestrians. The site test results show that the system is stable and reliable with high recognition accuracy, and can effectively monitor pedestrians within the range of 1 to 2 meters on the front left area of the conveyor at the back of the continuous shearer, which effectively guaranteed life safety of pedestrians nearby the continuous shearer.

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.)

The YOLOv3 algorithm uses Darknet53 as the backbone in the target detection (pedestrian detection) of a single object, and the network appears redundant, which results in too many parameters and slow detection speed. Additionally, the traditional bounding box loss function makes the detection and positioning inaccurate. To solve these problems, the improved YOLOv3 backbone network is proposed in the current study. A new multi-scale fusion network based on Darknet19 is constructed to accelerate the training speed and detection speed, and a generalized intersection over union loss function is introduced to improve the detection accuracy. The experimental results show that the proposed algorithm improves the accuracy of the original algorithm by 5% in the pedestrian detection dataset such as the INRIA pedestrian dataset. Compared with Faster R-CNN, the detection speed of a single image reaches 0.015 s per image under the condition of good accuracy. [ABSTRACT FROM AUTHOR]

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 South China University of Technology (Natural Science Edition) is the property of South China University of Technology 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 Information & Control is the property of Gai Kan Bian Wei Hui 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 Heilongjiang University of Science & Technology is the property of Journal of Heilongjiang University of Science & Technology 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 Heilongjiang University of Science & Technology is the property of Journal of Heilongjiang University of Science & Technology 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.)

Pedestrian detection is a task which is necessary among all tasks leveraged in automatic driving domain. Traditional pedestrian detection algorithms took fully advantage of image data, unable to obtain depth of objects. To address aforementioned issue, this paper proposed a method based on raw LiDAR point cloud data. The proposed method combined traditional moving object detection in LiDAR data and point cloud recognition by deep learning, and was capable of perception and pedestrian detection without images, obtaining 3D location of pedestrian, the refore helping central control system make reasonable decisions. This method experimented in 3D object detection task of KITTI dataset, obtained 33.37% AP (average precision) on mode rate cases. The results show that the proposed method performs better than other algorithm based on LiDAR data, which hence indicates the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology 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 Image & Graphics is the property of Editorial Office of Journal of Image & Graphics 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 Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui 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.)