Abstract
Introduction: In the target detection technology of underwater robots, many patents and papers have aimed to enhance the accuracy of underwater target detection, but limited resources in underwater robots overlook lightweight detection methods.
Method: In this study, we proposed an underwater target detection method using lightweight devices while ensuring high accuracy that could be maintained with limited resources. Our proposed algorithm leveraged the Ghost lightweight network, EMA mechanism, and CARAFE up-sampling technology to enhance YOLOv5s. To validate our method, comparative experiments, visual analysis, and ablation experiments were conducted.
Results: The experimental results showed that our algorithm had a model size of only 9.7 M, with 4.38×106 parameters and a computational volume of 8.4 GFLOPs. Precision, recall, and mAP@0.5 increased by 4.2%, 2.2%, and 2.5%, respectively.
Conclusion: Our improved algorithm provided an efficient and accurate solution for underwater robot target detection technology.
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