Abstract: To address the problem of missed detection in dense scenes and low detection rate for small-scale faces, an improved multi-face detection algorithm based on YOLOv5s, named IYOLOv5s-MF, is proposed. First, the feature texture transfer (FTT) module is introduced into the feature fusion part to obtain more feature representations for small-scale faces. Then, the positive and negative sample sampling strategy is improved by increasing the number of effective positive samples to enhance the model's generalization ability. Finally, Focal-EIoU is adopted as the localization loss function to accele- rate model convergence and improve face detection accuracy. Experimental results on the WIDER FACE dataset show that compared with other comparison algorithms, IYOLOv5s-MF has higher face detection accuracy and good real-time performance.

Key words: face detection, YOLOv5s, feature fusion, Focal-EIoU