Towards Accurate Face Detection Under Occlusion, Class Imbalance and Small-Scale Challenges

To address face occlusion, low detection rates of small-scale faces, and sample imbalance in dense visual scenarios, we propose a YOLOv7-based detector with four key improvements: (1) an optimized MPConv module to enhance feature extraction; (2) a novel CFPM to boost sensitivity to occluded samples; (3) an integration of the DyHead block in IDetect to mitigate feature loss from sample imbalance; (4) an SW-SCE loss function with a dual-input network to better detect small faces. Experiments on the WiderFace dataset show that our method improves detection performance by 1.2%, 1.8%, and 3% on the easy, medium, and hard subsets over the baseline. These gains strengthen face detection in dense, challenging environments with heavy occlusion and small-scale targets.