DB-YOLO:一个双分支并行工业缺陷检测网络.
Ziling Fan1, Yan Zhao1, Chaofu Liu1
1College of Architectural and Engineering, Yunnan Agricultural University, Kunming 650201, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了DB-YOLO,这是一种用于准确检测绝缘体缺陷的双分支算法,通过在复杂环境中有效识别小型和大型缺陷来改善功率检查.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 在电力系统中检测绝缘体缺陷至关重要,但由于缺陷大小和复杂的背景的变化而具有挑战性.
- 现有的方法难以同时准确识别小型和大型缺陷.
研究的目的:
- 开发一种新的算法,以在多个尺度上进行强大而精确的绝缘体缺陷检测.
- 提高自动化电力检查系统的准确性和效率.
主要方法:
- 提出了一种基于YOLO的双分支算法 (DB-YOLO),利用YOLOv11架构.
- 集成的Mamba机制,以改善全球特征表示,并将ShuffleNetV2作为轻量级的骨干.
- 采用了根据缺陷大小量身定制的自适应加权CIOU损失函数.
主要成果:
- 在CPLID,自建和GC-10数据集上,DB-YOLO在准确性和实时效率方面表现出卓越的性能.
- 双分支设计有效地应对了不同尺寸的缺陷所带来的挑战.
- 马巴机制和适应性损失功能有助于在杂乱的场景中增强检测.
结论:
- DB-YOLO在绝缘体缺陷检测技术方面取得了重大进展.
- 拟议的方法显示了在现实世界电力检查中实际应用的强大潜力.
- DB-YOLO在高精度和高效推断速度之间取得了平衡.
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