Research on Target Counting Based on Improved YOLOv5 and SORT Algorithms

In order to solve the statistical problem of some targets when fishing vessels are operating, based on deep learning technology, this paper uses the improved YOLOv5s and SORT algorithms to achieve target statistics. First, YOLOv5s is fused with CBAM and SE attention mechanism modules, respectively, to reduce the interference of complex backgrounds and improve the model detection accuracy simultaneously. Comparing the three models, the target detection model with a better effect is selected. Secondly, through the threshold method, SORT algorithm combined with the detection line and DeepSORT three algorithms to achieve the count of some targets, respectively. The results show that the accuracies of YOLOv5s, YOLOv5s fused CBAM, and YOLOv5s fused SE are 97.2%, 84.8%, and 98.9%, respectively. Among them, the YOLOv5s fusion SE module has the best effect, which is 1.7% and 14.1% higher than the other two results. Among the three target statistics methods, the SORT algorithm combined with the detection line is the best, with an average count accuracy rate of 85.7%. The count accuracy rates of the three categories of Fish_basket, Fish_net, and Process_ship are 96.5%, 85.8%, and 75%, respectively, compared with the other two species have improved significantly. The research results can provide an informational reference for the automated counting of targets during fishing vessel operations.