Gesture recognition based on modified Yolov5s

Abstract

With the development of artificial intelligence technology, human–computer interaction technology through gestures, images and voices has gradually become a hot topic for discussion. A modified Yolov5s gesture recognition method is proposed in the field of human–computer cooperation by optimizing the network structure of Yolov5s backbone, CNN is replaced by Ghostbottleneck module to increase the target occlusion recognition rate. Secondly, tensor stitching is added to the output of Ghostbottleneck module for up sampling to strengthen the reuse of image features. Finally, the detection ability of the improved model in the face of complex environment is verified on the self-made data set. Experimental results show that, the mAP@0.5 (mean average precision) of the modified Yolov5s is 94.49%, the AP (average precision) is 94.2%. By comparing the Yolov5s algorithm, Yolov4 algorithm,Yolov3 algorithm and SSD algorithm, the detection accuracy of the modified method has been significantly improved, which can fully meet the application requirements of real-time detection of gesture-controlled robots.