Developing lightweight object detection models for USV with enhanced maritime surface visible imaging

Abstract

Maritime surface object detection is a key technology for the autonomous navigation of unmanned surface vehicles (USVs). However, Maritime surface object detectors often face challenges such as large parameter sizes, object size variations, and image degradation caused by complex sea environments, severely affecting the deployment and detection accuracy on USVs. To address these challenges, this paper proposes the LightV7-enhancer object detection framework. This framework is based on the CPA-Enhancer image enhancement module and an improved YOLOv7 detection module for joint optimal learning. First, a new lightweight backbone network, GhostOECANet, was designed based on Ghost modules and improved coordinate attention. Second, by integrating ELAN and Efficient Multi-scale attention, an ELAN-EMA module is constructed to enhance the network’s perception and multi-scale feature extraction capabilities. Additionally, to improve the detection accuracy of small objects, multi-scale object detection layers are added based on the YOLOv5 detection head. The paper also introduces CPA-Enhancer in conjunction with the improved YOLOv7 detection module for joint training to adaptively restore degraded Maritime surface images, thereby improving detection accuracy in complex maritime backgrounds. Finally, the SeaShips dataset and Singapore Maritime Dataset are used to evaluate and compare LightV7-enhancer with other mainstream detectors. The results show that LightV7-enhancer supports object detection in various degraded maritime scenarios, achieving a balance between accuracy and computational complexity compared to other mainstream models. Compared to the baseline YOLOv7, LightV7-enhancer improves mAP by 2.7% and 7.5% on the two datasets, respectively, and has only half the number of parameters of YOLOv7, demonstrating robustness in degraded sea surface scenarios.