METHOD FOR ELIMINATING ABNORMAL KEY POINTS IN ATHLETE POSTURE RECOGNITION USING YOLOV7-POSE
This paper proposes several enhancements to the original Yolov7-pose algorithm:firstly,constructing a deep layer aggregation network(DLA)to replace the existing feature fusion network and enhance feature expression;secondly,fusing ELAN(efficient layer aggregation net)and SAAN(scale-aware attention net)into the Yolov7-pose algorithm;thirdly,introducing a scale factor into the loss function of the Yolov7-pose algorithm and determining its optimal value through experiments.Experimental results demonstrate that these improvements eliminate key point anomalies in posture recognition,achieving an accuracy rate exceeding 95％and operating efficiency surpassing 24FPS.The comprehensive performance surpasses that of the original Yolov7-pose algorithm and other algorithms significantly.
万方数据
维普
