Deep learning-based face detection method under low illumination conditions in coal mines

The low contrast and blurry facial features of facial images collected by the monitoring system are caused by dim illumination and interference from artificial illumination sources in coal mines. Traditional facial detection algorithms may cause false or missed detections when applied in coal mines. In order to solve the above problems, a deep learning-based face detection method under low illumination conditions in coal mines is proposed. A generative adversarial network (GAN) based on unsupervised learning is used to enhance the contrast of low illumination images in coal mines. A self-adjusting attention guided U-Net is used as the generator, and dual discriminators are used to guide global and local information. The self-feature retention loss function is used to guide the training process and maintain the texture structure of the face in the image and strengthen facial features. It can avoid phenomena such as exposure and loss of facial detail information, and obtain clearer facial images. The RetinaFace face detection framework is used to detect the enhanced facial features. It uses a feature pyramid structure and a single stage detection mode to detect facial images. It improves the capability to detect small-scale faces without increasing computational complexity. The experimental results on the public low illumination face dataset DARK FACE and the self built coal mine underground face dataset show that this method improves image contrast, clearly restores facial features in the image, and performs well in accuracy, recall, and average accuracy, effectively improving the accuracy of coal mine underground face detection.