BGLE-YOLO:一种用于水下生物检测的轻量级模型
Hua Zhao1,2, Chao Xu2, Jiaxing Chen1
1School of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050024, China.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
一个新的水下鱼类检测模型,BGLE-YOLO,在具有挑战性的水中条件下提供准确的物体识别. 这种高效的模型适用于边缘设备,在最小的计算成本下提供高性能.
科学领域:
- 计算机视觉 计算机视觉
- 海洋生物学 海洋生物学
- 机器人技术 机器人技术 机器人技术
背景情况:
- 水下环境对物体检测具有重大挑战,原因是对比度低和色差偏差.
- 自动化方法对于在水下图像中准确识别小物体至关重要.
研究的目的:
- 开发一个高效准确的水下物体检测模型,适用于边缘设备.
- 解决现有模型在检测小型和低对比度的水下物体方面的局限性.
主要方法:
- 引入一个高效的多尺度卷积式EMC模块来增强骨干网络.
- 将针对小目标 (BIG) 的全球和本地特征融合模块集成到子网络中.
- 构建一个轻量级的共享头部 (LSH),并进行重新参数化以保持精度.
主要成果:
- BGLE-YOLO模型在DUO和RUOD水下数据集上表现出高精度.
- 实现了与基准模型可比的准确性,计算成本大大降低 (6.2 GFLOPs).
- 该模型拥有1.6MB的超低参数数,非常适合边缘部署.
结论:
- BGLE-YOLO为自动水下物体检测提供了有效的解决方案,特别是对于小型和具有挑战性的目标.
- 该模型的轻量级设计和高效率使其适用于边缘设备上的实时应用.
- 这项研究有助于在海洋研究和水下机器人技术中推进计算机视觉应用.
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