Study on gesture recognition based on improved YOLOV8

Based on YOLOV8 hand gesture signal recognition in today's hand gesture signal recognition field has a great development and application prospects, which improve the detection accuracy of the hand gesture signal is the most important thing. Hand gesture signal recognition and detection and other areas of target detection is slightly different, hand gesture in the whole picture often belongs to a smaller target, and hand gesture signal recognition of complex application scenarios, In an effort to enhance the detection precision of hand gesture signal, this paper in initial YOLOV8 algorithm based on YOLOV8 algorithm: in YOLOV8 backbone network tail to join the Multi-Scale Convolutional Attention (MSCA) module is added to the tail of YOLOV8 backbone network; Large Selective Kernel Network (LSKNet) module is included into the front of YOLOV8 detection head; and the loss function of YOLOV8 is exchanged for Wise-IoU. The improved YOLOV8 is evaluated against initial YOLOV8 in control experiments, and the experimental results indicate that all the exactness measurements of YOLOV8 are enhanced in the detection and identification of hand gesture signal, and it can be proved that this paper's algorithm can enhance the exactness of YOLOV8. The experimental conclusions indicate that the improved YOLOV8 has enhanced all the accuracy measures in the recognition and detection of hand gesture signal, and it can be demonstrated that the improved YOLOV8 suggested in this paper has certain effect in the recognition and detection of hand gesture signal.