A Fast Tongue Detection and Location Algorithm in Natural Environment

The collection and extraction of tongue images has always been an important part of intelligent tongue diagnosis. At present, the collection of tongue images generally needs to be completed in a sealed, stable light environment, which is not conducive to the promotion of extensive tongue image and intelligent tongue diagnosis. In response to the problem, a new algorithm named GCYTD (GELU-CA-YOLO Tongue Detection) is proposed to quickly detect and locate the tongue in a natural environment, which can greatly reduce the restriction of the tongue image collection environment. The algorithm is based on the YOLO (You Only Look Once) V4-tiny network model to detect the tongue. Firstly, the GELU (Gaussian Error Liner Units) activation function is integrated into the model to improve the training speed and reduce the number of model parameters; then, the CA (Coordinate Attention) mechanism is integrated into the model to enhance the detection precision and improve the failure tolerance of the model. Compared with the other classical algorithms, Experimental results show that GCYTD algorithm has a better performance on the tongue images of all types in terms of training speed, tongue detection speed and detection precision, etc. The lighter model can contribute on deploying the tongue detection model on small mobile terminals.