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Infrared thermal radiation image defect detection and expression recognition application in badminton monitoring process

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 58卷

作者： Mao, Haobo Wen, Luguang Cao, Yecheng Zhao, Jianbo Jinhua Univ Vocat Technol Gen Educ Coll Dept Mil & Phys Educ Jinhua 321000 Zhejiang Peoples R China Shijiazhuang Univ Dept Phys Educ Shijiazhuang 050035 Hebei Peoples R China Shanxi Univ Coll Phys Educ Taiyuan 030006 Shanxi Peoples R China

With the development of science and technology, how to use advanced monitoring technology to improve athletes' performance and prevent sports injuries has become a research hotspot. The practical application of infrared thermal radiation image technology in badminton monitoring is studied, especially in the defect detection and expression recognition of players. In the process of badminton training and competition, the high resolution infrared thermal camera is used to capture the real-time thermal radiation image of athletes, and the expression of athletes is recorded by high-definition cameras. image processing software is used to preprocess infrared thermal radiation images, develop and apply specific algorithms to analyze thermal radiation images, identify abnormal hot areas, and match them with known patterns of muscle fatigue and injury. Using a machine learning approach, the model is trained to recognize and classify athletes' expressions to analyze their emotional states. The results show that the infrared thermal radiation imaging technology successfully identifies the abnormal hot areas of athletes during high-intensity training and competition, which are highly correlated with muscle fatigue and potential injury locations. The facial expression recognition technology accurately captures the emotional changes of athletes in different stages of the game, such as tension, excitement, fatigue, etc., and provides real-time emotional feedback for coaches. Combined with infrared thermal radiation imaging technology and expression recognition technology, coaches can adjust training plans and competition strategies more effectively, thus improving the overall performance of athletes. Through defect detection and expression recognition, the technology can not only help prevent sports injuries, but also improve the efficiency of training and competition.

关键词： Infrared thermal radiation image defect detection Expression recognition Badminton Motion monitoring

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Application of light sensors based on reinforcement learning in martial arts action optimization and image defect detection

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OPTICAL AND QUANTUM ELECTRONICS 2024年第4期56卷 498-498页

作者： Yan, PengLin Zhang, YinHang Yangzhou Univ Yangzhou 225127 Peoples R China

Wushu has unique movement requirements and techniques, but the traditional methods of optimizing wushu have some problems, such as unstable effect and difficult quantitative evaluation. In recent years, light sensors have been widely used in the field of image processing and detection, but there is little research on the application of martial arts motion optimization and image defect detection. The purpose of this study is to optimize martial arts movements and detect image defects by using light sensors based on reinforcement learning (RL) algorithm. The system collected the data of martial arts movement and image defects. Based on RL algorithm, the optimization model of optical sensor is established. The model is trained to optimize martial arts movements and detect image defects. The experimental results show that the light sensor based on RL can effectively optimize martial arts movements and accurately detect image defects. The proposed method improves the accuracy and fluency of martial arts movements in terms of optimization, and achieves high precision and efficiency in image defect detection, providing a new method and idea for martial arts training and image processing.

关键词： Reinforcement learning Light sensor Wushu teaching Motion optimization image defect detection

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The Application of One-Class Classifier Based on CNN in image defect detection

The Application of One-Class Classifier Based on CNN in Imag...

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Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS)

作者： Zhang, Mei Wu, Jinglan Lin, Huifeng Yuan, Peng Song, Yanan South China Univ Technol Sch Automat Guangzhou 510640 Guangdong Peoples R China Guangdong Univ Technol Coll Automat Guangzhou 510006 Guangdong Peoples R China

In the field of defect detection, image processing algorithms and feature extraction algorithms have some limitations, owing to their necessity for extracting a large number of different features of diverse products images. Meanwhile, the images of defective products are less and various. Aiming at these problems, we presented a One-Class classifier based on deep convolution neural network to detect the defect images in this paper. We design a loss function with the penalty term based on Euclidean distance to train the deep convolution neural network model. A hypersphere is used as classification decision surface after setting an appropriate hypersphere radius according to the inspection accuracy. It maps the non-defective products into a hypersphere in a high dimensional feature space, while the defect images are mapped somewhere far from the center of hypersphere. Thus, a One-Class classifier based on convolutional neural network(CNN) model is proposed to detect the defects. Experiments show that the proposed method, with less number of iteration, help build the classifier for image defect detection with high generalization ability and high detection precision. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： One-Class classifier CNN image defect detection Hypersphere

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A New Local Homogeneity Analysis Method based on Pixel Intensities for image defect detection 2

A New Local Homogeneity Analysis Method based on Pixel Inten...

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IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS)

作者： Rajitha, B. Tiwari, Anjana Agarwal, Suneeta MNNIT Allahabad Allahabad Uttar Pradesh India

ISBN: (纸本)9781479983490

this paper presents a new technique for defect detection in various images like fabric, paper, metal surface, film surface, nonwoven, coated coil and glass surface etc images. To detect such unstructured defects, it is required to analyze the underlying texture information of the image. All of these images have some similar texture properties. These texture features can be achieved through local homogeneity analysis (H-image) such as edges and boundaries which arise due to sudden changes in intensity levels of pixels. The defected regions can be one or more in a texture based image which may vary in shape and gray levels as well, thus the defect detection is a challenging task. Traditional homogeneity based approaches gives the fine edge details as well, some of them can be treated as a defect which is not a defect. So, this paper proposes a new technique to find the local homogeneity image using pixel intensity differences among neighboring pixels. Thereafter for thresholding a DWT (Discrete Wavelet Transform) and Hotelling T-2 model are used. The performance of the proposed approach has been evaluated against various measures like precision, specificity, recall, accuracy and error rate. In comparison to the existing H-image the proposed approach had 98% accuracy when tested on 100 different types of images.

关键词： image segmentation image defect detection HIPI image wavelet transform (WT) Hotelling T-2 model

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The Application of One-Class Classifier Based on CNN in image defect detection

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Procedia Computer Science 2017年 114卷 341-348页

作者： Mei Zhang Jinglan Wu Huifeng Lin Peng Yuan Yanan Song South China University of Technology School of Automation Guangdong Guangzhou 510640 China P.R Guangdong University of Technology College of Automation Guangdong Guangzhou 510006 China P.R

关键词： One-Class classifier CNN image defect detection Hypersphere

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Automated in-camera detection of flash-eye defects

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2005年第1期51卷 11-17页

作者： Corcoran, P Bigioi, P Steinberg, E Pososin, A FotoNation Ireland Ltd Galway Ireland FotoNation Inc San Francisco CA USA

This paper examines the problem of performing automated real-time detection of flash eye defects (redeye) in the firmware of a digital camera. Several different algorithms are compared, timing and memory requirements on several embedded architectures are presented. A discussion on advanced in-camera techniques to improve on standard algorithms is also presented.

关键词： digital camera image processing redeye image defect detection

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Investigation of the Variability in the Assessment of Digital Chest X-ray image Quality

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JOURNAL OF DIGITAL IMAGING 2013年第2期26卷 217-226页

作者： Whaley, Jacquelyn S. Pressman, Barry D. Wilson, Jonathan R. Bravo, Lionel Sehnert, William J. Foos, David H. Carestream Hlth Inc Clin Applicat Res Rochester NY 14615 USA Cedars Sinai Med Ctr S Mark Taper Fdn Imaging Ctr Dept Imaging Los Angeles CA 90048 USA

A large database of digital chest radiographs was developed over a 14-month period. Ten radiographic technologists and five radiologists independently evaluated a stratified subset of images from the database for quality deficiencies and decided whether each image should be rejected. The evaluation results showed that the radiographic technologists and radiologists agreed only moderately in their assessments. When compared against each other, radiologist and technologist reader groups were found to have even less agreement than the inter-reader agreement within each group. Radiologists were found to be more accepting of limited-quality studies than technologists. Evidence from the study suggests that the technologists weighted their reject decisions more heavily on objective technical attributes, while the radiologists weighted their decisions more heavily on diagnostic interpretability relative to the image indication. A suite of reject-detection algorithms was independently run on the images in the database. The algorithms detected 4 % of postero-anterior chest exams that were accepted by the technologist who originally captured the image but which would have been rejected by the technologist peer group. When algorithm results were made available to the technologists during the study, there was no improvement in inter-reader agreement in deciding whether to reject an image. The algorithm results do, however, provide new quality information that could be captured within a site-wide, reject-tracking database and leveraged as part of a site-wide QA program.

关键词： image quality quality assurance quality control digital radiography computed radiography algorithms reject analysis repeat analysis image defect detection

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defect detection of Printing images on Cans Based on SSIM and Chromatism 3

Defect Detection of Printing Images on Cans Based on SSIM an...

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3rd IEEE International Conference on Computer and Communications (ICCC)

作者： Zhou, Maomin Wang, Guijin Wang, Jinnan Hui, Changqing Yang, Wenming Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Grad Sch Dept Elect Engn Shenzhen Peoples R China

ISBN: (纸本)9781509063529

In the production of digital printing equipment, it is a challenge to detect the printed surface defects of cylindrical surface like cans. This paper presents an automatic inspection program based on computer vision. We build a set of printing detection hardware system including the automatic image acquisition, high precision image registration. Then we propose a geometric defect detection based on SSIM and color defect detection algorithm based on CIEDE2000 color difference formula. The experimental results show that the system proposed by us has good effectiveness and robustness, and can realize digital detection and feedback adjustment.

关键词： image defect detection registration SSIM color difference

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Overview of UAV-Based Overhead Power Line defect detection Based on Deep Learning 6

Overview of UAV-Based Overhead Power Line Defect Detection B...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Tian, Xiaoxi Wang, Xin Liu, Zhenpeng Peng, Baoquan Liu, Yuwei School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Shenyang Jianzhu University University Government Departments Shenyang China

ISBN: (纸本)9798350374315

Unmanned Aerial Vehicles (UAV) equipped with various sensors can efficiently and safely perform automated detection of overhead power lines. Deep learning-based image recognition technology plays a crucial role in this field, as it can automatically identify and classify power Line defects, enhancing the accuracy and efficiency of the detection process. This paper first outlines the main procedures of UAV-based overhead power line defect detection. It then provides an overview of the key technologies in deep learning-based UAV detection, focusing on algorithm analysis, dataset construction, evaluation metrics, and model lightweight. Finally, it discusses the challenges and prospects of deep learning-based UAV detection for overhead power line defects. © 2024 IEEE.

关键词： Algorithm optimization Data set construction Deep learning image defect detection Model lightweight UAV detection

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Surface defect detection of stay cable sheath based on autoencoder and auxiliary anomaly location

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： Liu, Qi He, Deqiang Shen, Yixin Lao, Zhenpeng Ma, Rui Li, Jianhui Guangxi Univ Sch Mech Engn Guangxi Key Lab Mfg Syst & Adv Mfg Technol Nanning 530004 Peoples R China Guangxi Acad Sci Inst Adv Equipment Mfg Nanning 530007 Peoples R China

As the critical protection component of stay cables, the stay cable sheath's reliability relates to cable-stayed bridges' operation safety. Due to the scarcity of defect samples, the subtlety of defect characteristics, and the high annotation cost in the actual industrial scene, existing methods are still challenging in automating the stay cable sheath's surface defect detection. To solve these problems, a semi-supervised deep learning method based on autoencoder and assisted anomaly location (AEAL) is proposed. The defect detection and localization task can be performed end-to-end with only normal samples used for training. The model learns the differences between normal and abnormal samples by constructing effective positive and negative sample pairs. Meanwhile, it fuses and fine-adjusts image features in combination with the proposed auxiliary anomaly location module and multi- scale feature fusion module, thereby achieving accurate location of defects. The experimental results demonstrate that AEAL outperforms other advanced unsupervised and semi-supervised defect detection networks, yielding superior defect location results. It achieves the highest pixel-level detection accuracy in the dataset of stay cable sheath surfaces, making it well-suited for practical applications.

关键词： Stay cable image defect detection Semi-supervised deep learning Autoencoder Anomaly location

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