Attention-guided Dual Enhancement Train Driver Fatigue Detection Based on MTCNN

The train driver’s fatigue driving will affect the normal operation of the train, and even threaten the life and property safety of the driver, passengers and the surrounding environment. Therefore, it is very important to detect whether the train driver has fatigue. However, when the light intensity is weak, the accuracy of fatigue detection will be low. In order to avoid the influence of low light on fatigue detection, a low light enhanced fatigue detection algorithm is proposed. Firstly, low light enhancement is performed on the collected driver’s face video image, so as to complete exposure enhancement and image denoising; Secondly, multi task cascaded convolutional neural network (MTCNN) is used to detect face and locate key points; Then, the eye and mouth positions of the key parts in the driver’s face are located, and the corresponding eye and mouth fatigue characteristic parameters are extracted; Finally, two fatigue characteristic parameters are fused to judge the fatigue of train drivers according to PERCLOS criterion and fuzzy reasoning principle. The experimental results show that the proposed method can accurately detect the driver’s fatigue state under low light conditions, and the accuracy has been greatly improved.