Fast Tongue Detection Based on Lightweight Model and Deep Feature Propagation

While existing tongue detection methods have achieved good accuracy, the problems of low detection speed and excessive noise in the background area still exist. To address these problems, a fast tongue detection model based on a lightweight model and deep feature propagation (TD-DFP) is proposed. Firstly, a color channel is added to the RGB tongue image to introduce more prominent tongue features. To reduce the computational complexity, keyframes are selected through inter frame differencing, while optical flow maps are used to achieve feature alignment between non-keyframes and keyframes. Secondly, a convolutional neural network with feature pyramid structures is designed to extract multi-scale features, and object detection heads based on depth-wise convolutions are adopted to achieve real-time tongue region detection. In addition, a knowledge distillation module is introduced to improve training performance during the training phase. TD-DFP achieved 82.8% mean average precision (mAP) values and 61.88 frames per second (FPS) values on the tongue dataset. The experimental results indicate that TD-DFP can achieve efficient and accurate tongue detection, achieving real-time tongue detection.