Improving the Accuracy of Robot Collecting Organisms in Marine Environment Based on Yolov5 Improvement

Abstract

AbstractIn this paper, an improved YOLOv5 multiscale marine organism target detection algorithm (YOLOv5‐Mult) is proposed to address the insufficient feature extraction ability of small targets, low detection accuracy, and high catching error of existing models in complex environments. First, real frame clustering is performed using the Kmeans++ method. Second, the BiFPN network module is adopted in lieu of the PANet network module to enhance the feature fusion ability. Next, the multilayer semantic fusion module RBC (RepBlock CSP) replaces the C3 module before the SPP layer of the Backbone network and the C3 module in the Neck layer to enrich the image semantic information. Finally, the multiscale feature fusion module MC (Mult Conv) replaces the last C3 module in the Backbone network to mitigate the semantic gap between different feature channel layers. Experimental results demonstrate that the improved algorithm attains a mAP value of 71.18%, which is 5.22% higher than that of the original YOLOv5 algorithm, providing accurate identification and fishing for underwater robots.