Research on Improved RetinaFace Fast Single Face Detection Algorithm
In certain specialized scenarios,there is a need for rapid and singular facial detection,such as i-dentity verification,facial tracking,and fatigue detection during driving.To address these problems,this paper proposes a detection method based on RetinaFace through the study and analysis of lightweight networks.Initial-ly,an efficient lightweight network,MobileNetV3,is selected to construct the backbone network.Subsequently,an ECANet is introduced between the backbone network and the FPN(Feature Pyramid Network)layer to en-hance feature extraction capabilities.Finally,adjustments are made to the FPN layer to improve detection speed.Experimental results indicate that the improved algorithm model shows an average precision increase of 3.6%and 5.7%on the simple and moderately challenging subsets of the WiderFace dataset respectively.Addi-tionally,the detection speed is enhanced by 21.6%and 13.1%.These results demonstrate that the proposed RetinaFace-based detection method,leveraging MobileNetV3 as the backbone network and incorporating ECANet for enhanced feature extraction,achieves notable improvements in both precision and speed,particularly in sce-narios like identity verification,facial tracking,and fatigue detection during driving.
face detectionlightweight networkRetinaFaceMobileNetV3ECANetFPN
NETL
NSTL
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