GCSEM-YOLO small scale enhanced face detector based on YOLO - Zheng, Xuwen; Zhou, Zhiwei; Photong, Chonlatee (Edelweiss Applied Science and Technology, 2025)
Face detection is a crucial aspect of computer vision, often challenged by factors such as varying scales, occlusions, and diverse facial features. In this study, we introduce GCSEM-YOLO, an innovative real-time face detection method built upon the YOLOv8 architecture. This approach incorporates a novel feature extraction module (GCSEM) alongside a specialized small-scale detection head, designed to capture pixel information across multiple levels and enhance the receptive field, thereby improving the accuracy of small face detection. To address the imbalance between easy and difficult samples, we employ an adaptive anchor box filtering algorithm coupled with the new WIoUv3 loss function. Experimental results demonstrate that GCSEM-YOLO achieves outstanding efficiency on the WIDER FACE validation datasets.