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Optical tracking sensor technology has been widely used in the field of motion. However, there are still some challenges in the recognition of badminton players, and more accurate methods are needed to capture the dynamic characteristics of badminton players. The aim of this study is to design an optical tracking sensor system based on image feature extraction for badminton player motion recognition. In this paper, a high resolution camera is used to collect the image sequence of badminton match. Then through image processing and computer vision technology, the key image features are extracted from the image sequence. Then, machine learning algorithm is used to classify and recognize the extracted features to achieve accurate recognition of badminton players' movements. The experimental results show that the optical tracking sensor system can effectively extract the movement features of badminton players and identify their movements accurately. Compared with the traditional method, the system in this paper has higher precision and real-time performance, and can meet the needs of practical applications. [ABSTRACT FROM AUTHOR]

Image generation has always been one of the important research directions in the field of computer vision. It has rich applications in virtual reality, image design, and video synthesis. Our experiments proved that the proposed multi-style image generative network can efficiently generate high-quality images with different artistic styles based on the semantic images. Compared with the current state-of-the-art methods, the result generation speed of our proposed method is the fastest. In this paper, we focus on implementing arbitrary style transfer based on semantic images with high resolution ( 512 × 1024 ). We propose a new multi-channel generative adversarial network which uses fewer parameters to generate multi-style images. The network framework consists of a content feature extraction network, a style feature extraction network, and a content-stylistic feature fusion network. Our qualitative experiments show that the proposed multi-style image generation network can efficiently generate semantic-based, high-quality images with multiple artistic styles and with greater clarity and richer details. We adopt a user preference study, and the results show that the results generated by our method are more popular. Our speed study shows that our proposed method has the fastest result generation speed compared to the current state-of-the-art methods. We publicly release the source code of our project, which can be accessed at https://github.com/JuanMaoHSQ/Multi-style-image-generation-based-on-semantic-image. [ABSTRACT FROM AUTHOR]

The detection of surface defects is crucial to industrial manufacturing. In recent years, numerous detection methods based on computer vision have been successfully applied in the industry. However, industrial defect detection is still full of challenges. In one aspect, most of the industrial defects are extremely small. In another aspect, even though the intra-class defects have numerous similar elements, their outward appearances differ significantly. In this paper, we propose a dual-structure attention-based multi-level feature fusion network (DaMFFN) to address these two issues. In the first attention-based multi-level feature extraction structure, we introduce novel attention pooling to preserve more detailed information about the defective features of the tiny defect by giving certain regions varying weights. In the second attention-based multi-level feature fusion structure, we propose channel attention to capture the defect feature with the greatest potential for discrimination rather than all possible defect features, which is employed to prevent the incorrect detection of intra-class defects. The experiments demonstrate that the detection performance of the DaMFFN is better than other methods in five surface defect datasets. [ABSTRACT FROM AUTHOR]

Shape recognition is an active research topic in the field of computer vision and graphic computing. Nevertheless, existing methods are still poor in accuracy and efficiency in some extent, which greatly limits their application in computer vision system. This paper investigates the restraint of feature structure that intrinsically deteriorates recognition performance. Furthermore, we propose a fast shape recognition method based on a bi-level restraint reduction of contour coding (CC2RR), which provides more effective theoretical support for the practical application of the visual algorithm. CC2RR reduces restraint performed from contour feature extraction and expression, respectively. First, for shape contour, the restraint of contour feature extraction is reduced by transforming the direction of contour points to contour segments; second, for the encoded contour segment, the restraint of the contour feature expression is reduced; in other words, the current direction is reduced to the previous and the next direction. Guided by these insights, Hamming code distance is used to match the coding features after the twofold restraint reduction, and the results are obtained. Experimental results verify that the method significantly improves the performance, which runs up to 500 times faster than the existing description methods based on shape contours while increasing robustness. This makes the method useful in practical software system. [ABSTRACT FROM AUTHOR]

In the natural environment, the detection and recognition process of Punna navel orange fruit using machine vision systems is affected by many factors, such as complex background, uneven light illumination, occlusions of branches and leaves and large variations in fruit size. To solve these problems of low accuracy in fruit detection and poor robustness of the detection algorithm in the field conditions, a new object detection algorithm, named Retinanet_G2S, was proposed in this paper based on the modified Retinanet network. The images of Punna navel orange were collected with Microsoft Kinect V2 in the uncontrolled environment. Firstly, a new Res2Net-GF network was designed to replace the section of feature extraction in the original Retinanet, which can potentially improve the learning ability of target features of the trunk network. Secondly, a multi-scale cross-regional feature fusion grids network was designed to replace the feature pyramid network module in the original Retinanet, which could enhance the ability of feature information fusion among different scales of the feature pyramid. Finally, the original border regression localization method in Retinanet network was optimized based on the accurate boundary box regression algorithm. The study results showed that, compared with the original Retinanet network, Retinanet_G2S improved mAP, mAP50, mAP75, mAPS, mAPM and mAPL by 3.8%, 1.7%, 5.8%, 2.4%, 2.1% and 5.5%, respectively. Moreover, compared with 7 types of classic object detection models, including SSD, YOLOv3, CenterNet, CornerNet, FCOS, Faster-RCNN and Retinanet, the average increase in mAP of Retinanet_G2S was 9.11%. Overall, Retinanet_G2S showed a promising optimization effect, particularly for the detection of small targets and overlapping fruits. [ABSTRACT FROM AUTHOR]

Optical processing technology can improve the accuracy and efficiency of image processing. Visual packaging art design simulation is to quickly generate realistic packaging effects in the early stage of packaging design, so as to make better design decisions. Traditional simulation methods often require a lot of time and human resources, and the results lack realism and detail. Machine vision technology based on optical processing can make full use of image processing algorithms and computer vision technology to achieve efficient and accurate packaging design simulation. In this paper, a machine vision technology framework based on image optical processing is proposed. In the image acquisition stage, high-resolution cameras are used to acquire real packaging images, and in the optical processing stage, algorithms are used to enhance, reduce noise and correct images to improve image quality and clarity. In the image analysis stage, machine learning algorithms are used to extract features and detect and identify packaging elements, providing data support for subsequent simulation rendering. The results show that the machine vision technology based on image optical processing can generate high quality and realistic simulation effect of packaging design. Compared with traditional methods, this technique has achieved significant improvements in terms of time cost and design quality. [ABSTRACT FROM AUTHOR]

Object detection from UAV (unmanned aerial vehicle) images is a crucial and challenging task in the field of computer vision. The task suffers from the difficulties of small dense objects, low pixel occupation of objects, and features that are not easily extracted in images. In this paper, we proposed a multilayer feature fusion algorithm named SCA-YOLO (spatial and coordinate attention enhancement YOLO) for small object detection with hybrid attention mechanisms. It uses the single-stage detection algorithm YOLOv5 as the base framework. Firstly, a hybrid attention module with associated coordinate attention is designed to enhance the feature extraction of small objects. Secondly, to address the problem that small objects are vulnerable to being disturbed by the complex background information on UAV images, an improved SEB (simple and efficient bottleneck) module is designed to further distinguish foreground and background features. Thirdly, a multilayer feature fusion structure is built to perform channel stitching of shallow and deep feature maps, as well as to enrich the semantic information of shallow features by adding horizontal jump connections. Finally, experiments are conducted on the VisDrone2020 dataset, which involves a large number of small objects photographed by drones. In addition, we also conduct extended experiments on the DOTA dataset and PASCAL VOC dataset. Comparative experimental results indicate that the proposed method considerably improves the accuracy of small object detection on multiple benchmark datasets. [ABSTRACT FROM AUTHOR]

Computer vision methods for non-contact detection of herd pigs could help detect early disease and reduce mortality rates by analyzing pig behavior. Due to the limitation of breeding space and cost, the unit breeding area is relatively dense, making it difficult to detect all pigs for a long time without interruption. In order to improve the detection performance, this paper proposes an end-to-end efficient and accurate herd pig detection framework based on YOLOv7 target detection model, which is named Ghost-YOLOv7-SIoU. In this framework, the feature extraction backbone network consists of a series of directly connected efficient layer aggregation networks (ELAN) and downsampling modules. The neck network contains a feature pyramid network and path aggregation network. Ghost convolution is adopted to replace the 3 × 3 standard convolution of the ELAN module in backbone network and the scaled-up ELAN module in neck network to obtain rich features while reducing the parameter number and computational effort. Furthermore, to speed up the model convergence and improve the model robustness and accuracy, SIoU loss is used for bounding box regression in the training stage. On the VOC2012 dataset, the number of parameters and floating-point operations decreased by 13.4% and 15.7% compared to YOLOv7, with comparable detection accuracy. Additionally, the number of parameters and floating-point operations decreased by 13.7% and 16.1% on our pig dataset. Ghost-YOLOv7-SIoU is superior to YOLOV4-CSP and YOLOR-CSP in accuracy. Experimental results demonstrate the effectiveness of the proposed method in improving the efficiency of model detection while ensuring detection accuracy. [ABSTRACT FROM AUTHOR]

No-reference (NR) image quality assessment (IQA) is an important task of computer vision. Most NR-IQA methods via deep neural networks do not reach desirable IQA performance and have bulky models which make them difficult to be used in the practical scenarios. This paper proposes a lightweight transformer and multi-head prediction network for NR-IQA. The proposed method consists of two lightweight modules: feature extraction and multi-head prediction. The module of feature extraction exploits lightweight transformer blocks to learn features at different scales for measuring different image distortions. The module of multi-head prediction uses three weighted prediction blocks and an FC layer to aggregate the learned features for predicting image quality score. The weighted prediction block can measure the importance of different elements of input feature at the same scale. Since the importance of feature elements at the same scale and the importance of the features at different scales are both considered, the module of multi-head prediction can provide more accurate prediction results. Extensive experiments on the standard IQA datasets are conducted. The results show that the proposed method outperforms some baseline NR-IQA methods in IQA performance on the large image datasets. For the model complexity, the proposed method is also superior to several recent NR-IQA methods. [ABSTRACT FROM AUTHOR]

In order to address the problem of low efficiency and accuracy of artificial visual inspection for the quality of toy sets, this paper investigates the defect detection of toy sets based on machine vision. Firstly, an improved U-Net (IU-Net) is developed by introducing the Squeeze-and-Excitation (SE) block into the U-Net and modifying its objective function. Then, a convolutional neural network (CNN) is constructed for feature extraction and defect matching. Finally, a defect detection model for toy sets is created by integrating these two models. In addition, when it comes to a new-variety toy set, the IU-Net model is trained through transfer learning with a small number of new-variety data samples. This can reduce the dependence on new-variety data samples and improve the efficiency of model training process. The experimental results show that the defect detection accuracy of the proposed method is 100% and 99.43% for the whole toy set and single component, respectively, which meets the requirements of industrial automation quality inspection of toy sets. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is a technology that recognizes human emotions based on biometric markers. Over the past decade, FER has been a popular research area, particularly in the computer vision community. With deep learning (DL) development, FER can achieve impressive recognition accuracy. In addition, favorable Internet-of-Things (IoT) advancements generate massive amounts of visual data needed to enable reliable DL-based emotion analysis. However, training DL models can suffer from significant memory consumption and computational costs, complicating many vision tasks. Additionally, the direct use of RGB images during the training and inference stages might raise privacy concerns in various FER applications. On the other hand, adopting large deep networks hampers quick and accurate recognition on resource-constrained end devices such as smartphones. As a viable solution, edge computing can be employed to bring data storage and computation closer to end devices rather than relying on a central cloud server. As a result, it can potentially facilitate the deployment of real-time FER applications since the latency and efficiency problems are well addressed by utilizing the computing resources at the edge. In this paper, we develop an efficient FER framework that integrates DL with edge computing. Our framework relies on facial landmarks to enable privacy-preserving and low-latency FER. Accordingly, various landmark detection models and feature types are studied empirically to investigate their capabilities in capturing the dynamic information of facial expressions in videos. Then, using the extracted landmark-based features, we design lightweight DL models to classify human emotions on IoT devices. Extensive experiments performed on benchmark datasets further validate the efficiency and robustness of our framework. [ABSTRACT FROM AUTHOR]