Location of key points in infrared palm image based on MDFPF-ResNet

As an auxiliary diagnostic method that can infer the health status of human organs, hand diagnosis plays an important role in the development of traditional Chinese medicine and intelligent traditional Chinese medicine. In order to solve the problem of inaccurate positioning of key points in infrared image, a residual network based on multi-scale hole convolution feature pyramid fusion is proposed to locate key points in infrared palm image. In each feature pyramid fusion module, the parallel structure of hole convolution pyramid level and improved bottleneck module is adopted, which increases the receptive field and improves the generalization ability of residual network; multiple multi-scale hole convolution feature pyramid fusion modules are cascaded to gradually obtain high-level semantic features, and full connection layer and dropout layer are added at the output end to obtain high-level semantic features Key point positioning coordinates. The experimental results show that the proposed method has better positioning performance and stronger robustness in infrared palm image.