A face detection method via ensemble of four versions of YOLOs

Abstract

We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. Then, we ensemble their results by two methods, namely, WBF (Weighted boxes fusion) and NMW (Non-maximum weighted). The experimental analysis showed that the mAP increases in the WBF ensemble of the models for all the easy, medium, and hard images in the datasets by 7.81%, 22.91%, and 12.96%, respectively. These numbers are 6.25%, 20.83%, and 11.11% for the NMW ensemble.