Pain Expression Recognition Based on Improved YOLOv5s
This paper proposes a lightweight facial pain expression recognition algorithm for mobile terminal devices.Firstly,the convolution of Ghost modules in GhostNet network structure is introduced to compress the number of parame-ters in the model and reduce the calculation cost.Then the SiLu activation function is replaced by the improved FReLu ac-tivation function to improve the identification accuracy and detection efficiency.Finally,CA attention mechanism is introduced into the output end of the backbone network to increase attention to the facial pain expression feature area and improve the recognition accuracy of pain expression model.The experimental results show that the accuracy of the improved model can reach 96.9%.The detection time of each image is 53 ms,which is 18%shorter than that of YOLOv5s model.
万方数据
维普
