BSE-YOLO:一个增强的轻量级多尺度水下物体检测模型.
Yuhang Wang1, Hua Ye1, Xin Shu1
1School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究介绍了BSE-YOLO,一种轻量级的水下物体检测模型. 它提高了准确性,并降低了参数,以便在具有挑战性的海洋环境中提高性能.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 海洋技术 海洋技术
背景情况:
- 水下图像存在重大挑战,包括低对比度,模糊目标和对象聚类.
- 传统的物体检测方法难以准确,对于水下机器人来说,计算密集.
研究的目的:
- 为水下应用开发一种轻量级且准确的物体检测模型.
- 解决受限制的机器人环境中现有方法的局限性.
主要方法:
- 提出了一个改进的轻量级YOLOv10n模型,命名为BSE-YOLO.
- 用改进的双向特征金字塔网络 (Bi-FPN) 取代原来的子,以减少参数.
- 引入了多尺度注意力协同模块 (MASM) 和集成的高效多尺度注意力 (EMA),以增强特征感知和融合.
主要成果:
- 在URPC2020上,BSE-YOLO实现了83.7%的mAP@0.5,在DUO上实现了83.9%的mAP@0.5.
- 该模型将参数减少了2.47M,同时比YOLOv10n.提高了mAP@0.5的2.2%和3.0%.
- 显示了参数的显著减少 (约. 0.2 M) 与基线YOLOv10n.相比.
结论:
- BSE-YOLO在水下物体检测中提供了精度和轻量设计之间的有效平衡.
- 拟议的模型为有限资源的水下机器人对物体检测任务提供了可行的解决方案.
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