Research and improvement of underwater target detection algorithm based on YOLOv5
In the detection process of underwater target organisms,due to the poor underwater environment,the weak light in the water,and most of the underwater organisms appear in the form of small targets,which makes the current underwater target detection brings the problem of loss of accuracy,and in order to solve the corresponding problems,a YOLOv5s-water algorithm based on the im-provement of YOLOv5s is given to solve the problem.Firstly,the backbone layer(Backbone)part of YOLOv5s is changed by STR(Swin-Transformer)rotating window to improve the generalization ability of the model,which in turn solves the problems brought by the poor underwater environment and the change of detecting target morphology.The FCM attention mechanism,which is a combination of the FReLU activation function and the CBAM attention neural mechanism,is embedded into the Neck part of YOLOv5s to highlight the target features and suppress the secondary information,so as to improve the algorithm accuracy and enhance the feature extraction of small targets.For small-target detection,small-target detection heads are added to the YOLOv5 structure to improve the sensing field,which in turn improves the small-target detection accuracy.Simulation and experimental results show that the proposed method in-creases the detection accuracy P by 1.47%compared to YOLOv5s,mAP@0.5 rising by 2.76%and the effect of small target detection is obvious,which proves the effectiveness of the method.
Small goalsThe light is weakFReLU activation functionCBAM attention neural mechanismSwin-TransformerSmall target detection head
国家科技期刊平台
NSTL
万方数据
维普
