Ship target detection method based on improved YOLOv8

Chenyu Zhu; Weichao Liu; Yuan Liu

doi:10.1117/12.3026600

27 March 2024 Ship target detection method based on improved YOLOv8

Chenyu Zhu, Weichao Liu, Yuan Liu

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131051E (2024) https://doi.org/10.1117/12.3026600
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chenyu Zhu, Weichao Liu, and Yuan Liu "Ship target detection method based on improved YOLOv8", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131051E (27 March 2024); https://doi.org/10.1117/12.3026600

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KEYWORDS

Target detection

Data modeling

Detection and tracking algorithms

Small targets

Object detection

Education and training

Mathematical optimization

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