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

Abstract

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.