ArticleSensors (Basel, Switzerland)2025
BSE-YOLO: An Enhanced Lightweight Multi-Scale Underwater Object Detection Model.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Development of a Novel Convolution to Interactive Capture and Recalibration Enhancement Module for Underwater Fish Detection in Sensor Networks.Sensors (Basel, Switzerland) · 2026Article
- Structure-Enhanced Underwater Object Detection via Wavelet-Edge Collaboration and Selective Multi-Scale Fusion.Sensors (Basel, Switzerland) · 2026Article
- Seafood Object Detection Method Based on Improved YOLOv5s.Sensors (Basel, Switzerland) · 2025Article
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3 authors.
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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%
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