A Practical YOLOV5 Face Detector with Decoupled Swin Head

Face detection is a fundamental and practical problem in computer vision, which aims to indicate the face positions in a wild environment precisely. However, different from the generic object detection tasks, there are a large number of face samples that suffer from unconstrained poses, occlusion, extreame lights, or other harmful conditions. YOLOV5 is an incredible milestone in the object detection area, but still not powerful enough for the challenging samples. To ease these difficulties, in this paper, we customize DSH-YOLOV5, a practical face detector. Specifically, we integrate a decoupled head with swin transformer layers, whose self-attention mechanism has great potential to explore subtle interconnection details. Additionally, we use two context modules (CBAM and SSH) to enhance the performance of features. Furthermore, we design a novel copy-paste data augmentation to fit the above challenging scenes. Extensive experiments demonstrate that we achieve SOTA performance on WIDER FACE, FDDB, and PASCAL FACE with competitive computational costs. Moreover, under COVID-19, we add the breathing mask and gender classification branches based on the ROI Align to produce more practical face information.