Complex scene ship object detection based on improved YOLOv5

With the rapid development of computer vision technology, the application of deep learning models in real-time object detection is becoming increasingly widespread. This article proposes an improved YOLOv5 model for ship detection at sea. By introducing the C3_SCConv module and SKAttention mechanism, the feature extraction and fusion capabilities of the model have been enhanced. Extensive experiments on both daytime and nighttime ship datasets have shown that the improved model outperforms the original model in terms of detection accuracy, recall, and mean accuracy (mAP), especially when dealing with multi-scale targets. The research results of this article provide an efficient and reliable solution for maritime vessel inspection, which has important practical application value.