River vessel target detection based on improved Yolov8

How to enhance river vessel target detection for Unmanned surface vessels(USVs) with limited resources has always been a critical technology for the practical application of intelligent ship navigation. However, factors such as waves, lighting conditions, and the overlapping of multiple targets in complex river environments often have a significant impact on the detection and identification performance of river targets, especially small ones. In recent years, YOLOv8, as the latest model in the YOLO series, has been validated for its capabilities in river target detection. To further improve its detection performance in complex river environments, this paper proposes a river unmanned boat target detection method based on improved Yolov8. Due to the complexity of river environments and the interference of multiple targets, accurate detection of river targets is essential for unmanned boats to navigate obstacles and plan subsequent paths effectively. Therefore, improvements were made to the Yolov8 network to enhance the detection of river targets. In the improved Yolov8, AFPN (Asymptotic Feature Pyramid Network) is used in the Neck to strengthen the model's feature fusion capability and reduce the number of model parameters. BRA (Bi-Level Routing Attention) is utilized in the Backbone to better extract features. The improved YOLOv8 network can accurately identify targets in complex river environments and improve the detection accuracy, while various comparative experiments also validate the rationality and effectiveness of the improvements.