Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism

Abstract

A face detection method based on lightweight network and weak semantic segmentation attention mechanism is proposed in this paper, aiming at the problems of low detection accuracy and slow detection speed in face detection in complex scenes. K-means++ algorithm is employed to perform clustering analysis on YOLOv4 model prior frames in this paper, and smaller size prior frames are set to capture small face information to solve the missing detection problem of small face targets in scenes. The backbone network structure is improved by introducing Mobile Net lightweight network model, to reduce the number of parameters and calculation of the model and improve the detection speed. The convolutional block attention module model with dual attention mechanism is embedded to improve the sensitivity of the model to target features, which can suppress interference information and improve the accuracy of target detection. A dynamic enhancement attachment based on weak semantic segmentation is added in front of the detector head, whose output is used as the spatial weight distribution to correct the activation area, to suppress the false detection and missed detection caused by the decrease of extraction ability evoked by the pursuit of lightweight. The experimental results on WIDEFACE dataset indicate that this method not only can detect face in real time and with high accuracy, but also has better performance than other existing methods.