Abstract
The accuracy and efficiency of object detection is the key technology to spread autonomous driving. In order to address issues such as missed and false detection in traditional target detection algorithms, deep learning technology is used to optimize the ship target detection model as well as the attention mechanisms in this article. Specifically, this paper proposes an improved model based the traditional YOLOv5s algorithm, and a self-created ship dataset is also provided. To refine ship target detection, various attention mechanisms were implemented and compared, including CBAM, ECA, GAM, SimAM, and SK-Net. Through comparative analysis of the effects of these mechanisms, GAM was identified as the optimal choice. The experimental results indicate that the mean average precision (mAP) of detecting ship targets was augmented by 1.0% following the incorporation of GAM. The YOLOv5s-GAM model is therefore deemed as an effective target detection approach in enhancing the safety of ship autopilot, with potential applications in the future.
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