YOLOv7-Ship: A Lightweight Algorithm for Ship Object Detection in Complex Marine Environments
Zhikai Jiang, Li Su, Yuxin Sun
Abstract
Accurate ship object detection ensures navigation safety and effective maritime traffic management. Existing ship target detection models often have the problem of missed detection in complex marine environments, and it is hard to achieve high accuracy and real-time performance simultaneously. To address these issues, this paper proposes a lightweight ship object detection model called YOLOv7-Ship to perform end-to-end ship detection in complex marine environments. At first, we insert the improved “coordinate attention mechanism” (CA-M) in the backbone of the YOLOv7-Tiny model at the appropriate location. Then, the feature extraction capability of the convolution module is enhanced by embedding omnidimensional dynamic convolution (ODconv) into the efficient layer aggregation network (ELAN). Furthermore, content-aware feature reorganization (CARAFE) and SIoU are introduced into the model to improve its convergence speed and detection precision for small targets. Finally, to handle the scarcity of ship data in complex marine environments, we build the ship dataset, which contains 5100 real ship images. Experimental results show that, compared with the baseline YOLOv7-Tiny model, YOLOv7-Ship improves the mean average precision (mAP) by 2.2% on the self-built dataset. The model also has a lightweight feature with a detection speed of 75 frames per second, which can meet the need for real-time detection in complex marine environments to a certain extent, highlighting its advantages for the safety of maritime navigation.