Hand target detection based on improved YOLOv5

摘要

With the growing maturity of deep learning-based target detection algorithms, their deployment in intelligent service robots for target detection has become popular nowadays, in order to improve the precision of real-time hand detection and recognition by intelligent service robots, enabling them to detect hands accurately in a variety of environments. This paper proposes a hand detection method based on improved YOLOv5 deep convolutional neural network. YOLOv5s is selected as the base target detection model, the SE attention module is added to the network neck detection layer to guide the model to pay more attention to the channel features of small target to improve the detection performance, and the detection layer is added to enhance the feature learning ability of the network for target regions. The loss function of the detection model is optimised according to the hand image features to improve the confidence of the prediction frame. The experimental results show that the proposed hand detection method based on the improved YOLOv5 deep convolutional neural network can achieve a precision of 99.02%, which is 6.54% better than the original YOLOv5.