YOLO-SAR: An Enhanced Multi-Scale Ship Detection Method in Low-Light Environments

Nowadays, object detection has become increasingly crucial in various Internet-of-Things (IoT) systems, and ship detection is an essential component of this field. In low-illumination scenes, traditional ship detection algorithms often struggle due to poor visibility and blurred details in RGB video streams. To address this weakness, we create the Lowship dataset and propose the YOLO-SAR framework, which is based on the You Only Look Once (YOLO) architecture. As for implementing ship detecting methods in such challenging conditions, the main contributions of this work are as follows: (i) a low-illumination image-enhancement module that adaptively improves multi-scale feature perception in low-illumination scenes; (ii) receptive-field attention convolution to compensate for weak long-range modeling; and (iii) an Adaptively Spatial Feature Fusion head to refine the multi-scale learning of ship features. Experiments show that our method achieves 92.9% precision and raises mAP@0.5 to 93.8%, outperforming mainstream approaches. These state-of-the-art results confirm the significant practical value of our approach.