Ship target detection method based on improved YOLOv8

With the development of machine vision, deep learning-based target detection methods are crucial in maritime ship detection. Although some recognition methods, such as YOLO (you only look once) and SSD (single shot multibox detector) have achieved good results, there are still problems with low detection accuracy and easy missed detection and false detection for small moving targets at sea. This research proposes an optimized YOLOv8 algorithm with an attention mechanism to enhance feature extraction and fusion, and adds a detection head for small targets at sea. Then data augmentation methods are introduced. Finally, videos are recorded on the laboratory's visual simulation system and a data set is constructed. Experimental findings reveal that the optimized method 's mAP@0.5 on the self-made data set is 2.9%, higher than the original method, and the mAP@0.5 of the speedboat small target is improved by 9.5%.