Abstract
One of the research directions of target detection-based computer vision, in which small target detection is the key and difficult research direction in target detection. Traditional target detection algorithms include Faster RCNN, YOLO, SSD, etc., and there is a problem that indicators such as detection accuracy, false detection rate, and missed detection rate are not ideal for small target detection tasks. In order to improve the above problems, this paper proposes an improved target detection algorithm based on YOLOv5. First, the CBAM attention mechanism is introduced in the Backbone part to strengthen the important feature channels; then a detection layer is added to the network according to the characteristics of the data set to strengthen the extraction ability. Experiments show that the improved YOLOv5s_CS algorithm has a mAP value of 75.1% on the test set, which is 3.9% higher than the original YOLOv5s network.
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