Improvement of the key point detection algorithm based on yolov8
X. N. Li, liansun zeng, Lixin Zheng · 2023
Human key point detection has essential application prospects in human and computer interaction and disease prediction. However, in the current scenario, there is still some room for improvement in the accuracy of key point detection when facing complex scenes and some key points are missing. To address the above situation this paper proposes an improved YOLOv8-SW model based on the YOLOV8 model, adding the SA attention mechanism to improve the model's ability to extract channel and spatial features, replacing IOU with WIOU, and increasing the proportion of the model's weight on the general quality box. The validation results on the COCO2017 dataset show that the improved model can achieve better detection performance for complex scenes. Compared with the baseline method, the accuracy is improved by 1.0%, which is significant in promoting the practical application of human key point detection.