Abstract

Info

Author(s):: JI Changpeng; YANG Menghan; DAI Wei; School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China

Keywords:: Keywords: tongue multi-texture Faster RCNN deep learning deformable convolution attention mechanism transfer learning multi-target feature recognition

Abstract:: Abstract: A Faster RCNN based method is proposed to realize the recognition of tongue multi-texture more efficiently and in parallel. Deformable convolution is used to reshape the convolutional layer in the backbone extraction network and perform deformable pooling, in which the region shape can be adjusted according to the actual condition to extract target features, thereby reducing the missed detection rate. Then, the attention mechanism scSE is introduced to improve the texture expression ability by enhancing meaningful features. In the context of multi-target hybrid and large multi-scale differences, a weighted bidirectional feature pyramid network is used for feature fusion to improve the accuracy of target detection, and finally transfer learning is conducted. The experimental results show that the method achieves an average accuracy of 0.935 for all categories, which is 0.457 higher than the original Faster RCNN model, indicating that the improved model can effectively solve the problems of multi-target hybrid and multi-scale differences, and has high detection accuracy.