Towards intelligent shipping: image-enhanced ship detection and situation analysis in low-light scenes

Abstract

Aiming to address the problems of insufficient ship detection accuracy and high miss rate for small targets in water transport traffic situational awareness under low-light conditions, this paper proposes an EG-YOLO+ framework that integrates ship image enhancement and ship detection. The method achieves adaptive enhancement of low-light images through the unsupervised enhancement-low-light-image-network generative adversarial network (EnlightenGAN) model, effectively solving the problem of detail loss in traditional methods under extreme lighting conditions; subsequently, based on the latest You Only Look Once version 11 (YOLOv11) architecture, it innovatively introduces the squeeze-and-excitation channel attention mechanism, significantly improving the detection accuracy of small-target ships through dynamic feature channel reweighting. The experimental results on the self-constructed maritime dataset show that the proposed method can effectively identify image targets in low-light environments, even small targets, with a 6-percentage point improvement in mAP over the baseline YOLOv11.

Keywords

Ship image enhancement / ship detection / YOLOv11 / EnlightenGAN / ship trajectory analysis

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