Driver’s fatigue will affect the normal driving of the vehicle, and in serious cases will threaten the life safety of driver and passengers. Therefore, detecting whether the driver is fatigue can effectively protect people’s travel safety. In real scenario, generally, when the night light intensity is weak, the driver has a lot of time of fatigue driving, but the existing related detection algorithms cannot deal with the lighting problem, resulting in a low accuracy rate at night fatigue driving detection. Aiming at such problem, this study proposed a night-light fatigue driving detection algorithm based on low-light enhancement. Firstly, the LIME algorithm was used to perform low-light enhancement processing on the face image to improve the exposure of the image. Secondly, the face keypoint detection network was used to obtain the eye area of the image. Thirdly, the convolutional neural network was used to classify the eye area with open and closed eyes. Finally, the ratio of the number of eyes opened and closed per unit time is counted to determine whether the driver is in a fatigue state. The experimental results show that in the night environment, the detection algorithm proposed in this study improves the detection success rate by 15.38% compared with the existing algorithms, and achieves better results.