YOLO-ELR: A High-Precision Lightweight Object Detection Model in Marine Environment

Target detection in complex marine environments serves as a core technology for marine environmental monitoring, underwater search and rescue, and vessel collision avoidance. However, traditional detection methods struggle to accurately identify low-pixel marine targets (e. g. , small vessels, buoys) while balancing detection accuracy and computational efficiency in complex environments. To address the low accuracy and high computational costs of object detection in complex marine environments, this paper proposes YOLO-ELR, a lightweight model based on the YOLOv11 framework, designed to identify object categories and directional positions with enhanced precision while optimizing resource efficiency. The Efficient Multi-Branch Scale and Light Adaptive-weight downsampling (EMBSLaw) backbone network enhances multi-scale object detection in complex scenes by dynamically adjusting feature contributions through adaptive weight computation, while maintaining a lightweight architecture. To reduce computational parameters and complexity, a novel lightweight spatial multi-branch detector Lightweight Shared Convolutional Separamter BN Detection head (LSCSBD) is introduced. Furthermore, the RepGhostCSPELAN and Efficient Multi-Scale Conv (RGCELEMSC) module, integrating multi-neural networks, is proposed to improve detection accuracy and precision. Experimental results demonstrate that the YOLO-ELR model achieves an mAP@50 of 84. 87%, surpassing the baseline YOLOv11 by 3. 94% while reducing parameters by 35% and GFLOPs by 7. 56%, which validates the effectiveness in balancing detection accuracy and computational efficiency.