Abstract
To address the problems of false detection and the lack of accurate target bounding boxes in the process of detecting human ear recognition, a SE-YOLOv3 target detection algorithm with improved YOLOv3 is proposed. Based on the YOLOv3 algorithm, the effects of embedding the SE attention module into different positions of the model on the detection performance of the algorithm are studied and analyzed separately. It is proved that embedding the SE attention module in the parallel ResNet network in the backbone of the YOLOv3 model can effectively improve the detection accuracy of the algorithm. And it is experimentally demonstrated in CCU-DE and USTB human ear datasets.
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