YOLOv5-CCE:an Object Detection Algorithm Based on CA and EIoU
In order to reduce the false detection rate and missed detection rate of the YOLOv5 model in complex environments,a target detection model YOLOv5-CCE based on CA(Coordinate Attention)and EIoU(Efficient Intersection over Union)is proposed.Firstly,the coordinate attention mechanism CA is embedded in partial C3_2 module in the Neck network to enhance the feature extraction ability of the model;secondly,to improve the regression accuracy,an improved Focal CEIoU Loss based on Focal EIoU Loss is proposed.The experimental results show that on the PASCAL VOC 2007+2012 data set,the YOLOv5-CCE model maintains the parameters and calculations basically unchanged,compared with the original model of mAP@0.5 and mAP@0.5:0.95 and the accuracy rate respectively,its accuracy rate has increased by 1.4%,1.3%and 3.7%respectively.Therefore,the YOLOv5-CCE model can better adapt to the target detection task in complex envi-ronments.
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