Ship exhaust gas detection algorithm based on improved YOLOv5s-CAF

In recent years, with the development of economic globalization in the post-epidemic era, the shipping market capacity has made steady progress, and the accompanying problem of black smoke pollution from ship exhaust is still one of the objects of concern outside the industry. In order to improve the efficiency and accuracy of ship exhaust black smoke target detection. In this paper, the backbone network of YOLOv5s is improved, and a ship exhaust detection model YOLOv5s-CAF is proposed. Firstly, a small target detection layer is added to retain more shallow semantic feature information. At the same time, a simplified weighted bi-directional feature pyramid network Light-BiFPN is proposed to replace the original path aggregation network PANet, which realizes multi-scheduling feature fusion. Finally, a CA (Coordinate Attention) mechanism is embedded in front of the detection head to enhance the network's ability to localize the ship exhaust gas area. The results show that the Precision and mAP0.5 of the improved algorithm YOLOv5s-CAF reach 91.4% and 83.9%, respectively, which improve 9.8% and 7.6%, respectively, compared with the original model, verifying the effectiveness of the improved strategy in this paper.