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在无人机图像中检测绝缘体缺陷的RSP-YOLOv11n多模块优化算法

Bin Zheng¹, Niwat Angkawisittpan², Lu Huang¹

¹Faculty of Electrical Engineering, Hunan Mechanical & Electrical Polytechnic, Changsha, Hunan, China.

Scientific reports

|October 10, 2025

概括

一种新方法RSP-YOLOv11n通过精确检测绝缘体缺陷来改善无人机检查. 这种先进的模型提高了检测准确度,并减少了电力线维护中错过的故障.

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:

输电线路绝缘器缺陷对于可靠的电网运行至关重要.
无人驾驶飞行器 (UAV) 检查对于高效和安全的维护至关重要.
在无人机图像中准确识别缺陷是由于小目标和表面变化而具有挑战性的.

研究的目的:

提出一种新的深度学习模型RSP-YOLOv11n,用于在无人机图像中增强绝缘体缺陷检测.
与现有方法相比,提高检测准确度并减少错过的检测.
在各种绝缘体数据集和现实世界检查场景上验证模型的性能.

主要方法:

通过修改YOLOv11n架构开发了RSP-YOLOv11n.
集成了RCSOSA单元用于多尺度特征提取.
采用了SEA注意力机制,以改进表面缺陷检测.
添加了一个P2检测头来增强小目标检测能力.

主要成果:

在定制绝缘体数据集上,RSP-YOLOv11n在其他YOLO模型上表现出卓越的性能.
获得了提高的精度 (92.3%),回忆 (85.9%),F1得分 (89.0%),mAP@0.5 (91.2%) 和mAP@0.5:0.95 (61.7%).
在基准数据集 (CPLID,IDID,SFID) 上表现优于最先进的探测器,如DINO和RT-DETR.

结论:

RSP-YOLOv11n显著提高了绝缘体缺陷检测的准确性和概括性.
该模型在识别小缺陷方面表现出强大的能力,这对于无人机检查至关重要.
RSP-YOLOv11n是一个有前途的解决方案,用于实际的,现实世界的无人机基础的电力线维护.

关键词:

绝缘器缺陷检测检测绝缘器缺陷检测绝缘器缺陷检测在P2检测头上.在 RSP-YOLOv11n 中使用.无人机检查检查无人机检查

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