DSLL-Face: Distributed Supervision-Integrated Framework for Low-Light Face Detection

In low-light environments, human vision is severely limited by weak light sources, leading to significantly reduced visual capabilities. Similarly, in machine vision, low-light recognition tasks such as nighttime autonomous driving and surveillance tasks involving the detection of small faces in low-light conditions are more challenging than tasks in normal lighting. Current low-light face detection models lack adaptability to different low-light conditions, and the accuracy of face detection remains unsatisfactory. In this paper, we propose a novel face detection framework DSLL-Face, specifically designed to tackle the challenges of face detection in low-light environments. Our proposed DarkHead, featuring a specialized branch designed to predict the distribution of bounding boxes, thereby substantially enhances the supervision of bounding box localization. This innovative approach effectively resolves the issue of blurry bounding boxes and significantly increases the accuracy of predicted positions. We employ a novel loss function tailored for detecting small faces, enhancing the sensitivity and effectively addressing the blurriness issues in small face detection. Furthermore, we leverage the Channel Grouping and Partial Convolution block (CGP) to enhance multi-scale expression capabilities. We develop the EMNet-pro model with the aim of further enhancing images to improve their adaptability under various low-light conditions. Extensive experiments demonstrate that our model exhibits outstanding capability in low-light face detection on the DARK FACE dataset and achieves significantly better performance compared to existing state-of-the-art frameworks.