Face detection algorithm based on improved TinyYOLOv3 and attention mechanism

Abstract

An improved face detection method ground on TinyYOLOv3 algorithm is put forward in this paper in view of the low recognition rate of traditional face detection methods in complex background and the long detection time of existing face detection methods ground on deep learning The main network of TinyYOLOv3 is redesigned to extract more abundant semantic information, which is increasing the number of network layers. The deep separable convolution is used instead of the traditional convolution and the features of different network layers are fused. Meanwhile, the accuracy of face detection is guaranteed, size of this network model is reduced. The CIoU (complete intersection over union) loss is used on improving this original prediction loss of bounding box coordinates. The channel attention mechanism is integrated into the feature extraction network to improve the positioning accuracy and detection accuracy. The network convergence speed is accelerated by hyperparameter optimization and priori box clustering method. The face detection experiments were conducted on the Wider Face data sets. And the experimental results manifest that the proposed algorithm has high recognition accuracy in complex scenes. At the same time, the proposed algorithm is better than others in terms of detection speed and model size, and can meet the requirements of embedded devices.