BSE-YOLO: An Enhanced Lightweight Multi-Scale Underwater Object Detection Model

Abstract

Underwater images often exhibit characteristics such as low contrast, blurred and small targets, object clustering, and considerable variations in object morphology. Traditional detection methods tend to be susceptible to omission and false positives under these circumstances. Furthermore, owing to the constrained memory and limited computing power of underwater robots, there is a significant demand for lightweight models in underwater object detection tasks. Therefore, we propose an enhanced lightweight YOLOv10n-based model, BSE-YOLO. Firstly, we replace the original neck with an improved Bidirectional Feature Pyramid Network (Bi-FPN) to reduce parameters. Secondly, we propose a Multi-Scale Attention Synergy Module (MASM) to enhance the model’s perception of difficult features and make it focus on the important regions. Finally, we integrate Efficient Multi-Scale Attention (EMA) into the backbone and neck to improve feature extraction and fusion. The experiment results demonstrate that the proposed BSE-YOLO reaches 83.7% [email protected] on URPC2020 and 83.9% [email protected] on DUO, with the parameters reducing 2.47 M. Compared to the baseline model YOLOv10n, our BSE-YOLO improves [email protected] by 2.2% and 3.0%, respectively, while reducing the number of parameters by approximately 0.2 M. The BSE-YOLO achieves a good balance between accuracy and lightweight, providing an effective solution for underwater object detection.