Occlusion Face Detection Method in Natural Scene Based on Improved YOLOv3
ZHANG Yue
ZHANG Peng
MA Qinghua
Abstract：A new model DDH-YOLOv3 for the fast detection of occluded faces in complex scenes is proposed in this paper,under the complex conditions such as illumination changes,partial occlusion,and different face posture in natural scenes.Firstly,an improved multi-scale DRFBs visual field sensor module is introduced to enlarge the network receptive field and enhance the fea-ture extraction capability.Secondly,the CBAM module is specifically improved and ECA module is introduced to replace the chan-nel domain to reduce the complexity of the module,and parallel embedding is used to further improve the ability of the model to ex-tract occluded facial features.Finally,in order to diminish the missing rate of occlusive face,DIoU is used as the boundary frame loss function.The experimental results show that the improved model accuracy and FPS than YOLOv3 respectively increased by 5.69%and 28.81 s.At the same time,the detection performance is better compared with several other advanced target detection algo-rithms.Finally,the improved algorithms are ported to the ZYNQ platform for deployment and porting and showed good detection re-sults.
Keywords：occlusion face detectionYOLOconvolutional neural networkreceptive fieldattention module
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：6( 3507-3512 )