An Improved YOLOv5s Algorithm for Object Detection with an Attention Mechanism

To improve the accuracy of the You Only Look Once v5s (YOLOv5s) algorithm for object detection, this paper proposes an improved YOLOv5s algorithm, CBAM-YOLOv5s, which introduces an attention mechanism. A convolutional block attention module (CBAM) is incorporated into the YOLOv5s backbone network to improve its feature extraction ability. Furthermore, the complete intersection-over-union (CIoU) loss is used as the object bounding-box regression loss function to accelerate the speed of the regression process. Experiments are carried out on the Pascal Visual Object Classes 2007 (VOC2007) dataset and the Microsoft Common Objects in Context (COCO2014) dataset, which are widely used for object detection evaluations. On the VOC2007 dataset, the experimental results show that compared with those of the original YOLOv5s algorithm, the precision, recall and mean average precision (mAP) of the CBAM-YOLOv5s algorithm are improved by 4.52%, 1.18% and 3.09%, respectively. On the COCO2014 dataset, compared with the original YOLOv5s algorithm, the precision, recall and mAP of the CBAM-YOLOv5s algorithm are increased by 2.21%, 0.88% and 1.39%, respectively.