YOLOSeaShip: a lightweight model for real-time ship detection

ABSTRACT

With the rapid advancements in computer vision, ship detection models based on deep learning have been more and more prevalent. However, most network methods use expensive costs with high hardware equipment needed to increase detection accuracy. In response to this challenge, a lightweight real-time detection approach called YOLOSeaShip is proposed. Firstly, derived from the YOLOv7-tiny model, the partial convolution was utilized to replace the original 3×1 convolution in the ELAN module to further fewer parameters and improve the operation speed. Secondly, the parameter-free average attention module was integrated to improve the locating capacity for the hull of a ship in an image. Finally, the accuracy changes of the Focal EIoU hybrid loss function under different parameter changes were studied. The practical results trained on the SeaShips (7000) dataset demonstrate that the suggested method can detect and classify the ship position from the image more efficiently, with mAP of 0.976 and FPS of 119.84, which is ideal for real-time ship detection applications.

KEYWORDS:

• Ship detection
• deep learning
• YOLO
• lightweight model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data, models, and code generated or used during the study appear in the submitted article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No. 62102227), Zhejiang Basic Public Welfare Research Project (No. LZY22E050001, LZY22D010001, LGG19E050013, LZY21E060001,LTGC23E050001, LTGS23E030001, LZY24E050001), Science and Technology Major Projects of Quzhou (2021K29, 2022K56,2022K92,2023K221,2023K211).