POLD-YOLO:一种基于YOLO11的轻量级算法,用于在无人机空中图像中检测绝缘体缺陷
Bo Hu1, Fanfan Wu2, Pengchao Zhang1
1School of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了POLD-YOLO,这是一个高效的对象探测器,用于无人机检查绝缘体缺陷. 它以最小的参数实现高精度,在资源有限的平台上实现实时缺陷检测.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 在无人驾驶飞行器 (UAV) 图像中检测小型绝缘体缺陷是具有挑战性的,原因是低分辨率,复杂的背景和尺度变化.
- 现有的物体检测模型经常在资源有限的无人机平台上与性能退化作斗争.
研究的目的:
- 为无人机实时绝缘体缺陷检查开发一个高效和准确的模型.
- 为了解决当前探测器在处理低分辨率和复杂的空中图像方面的局限性.
主要方法:
- 提出了POLD-YOLO,这是一个基于YOLO11.11的轻量级物体探测器.
- 整合了一个与 PoolingFormer 和 Channel-wise Gated Linear Units (CGLUs) 的骨干.
- 引入了全维自适应低采样 (OD-ADown) 和轻量级共享卷积检测头 (LSCD-Head).
- 使用了Focaler-MPDIoU损失函数来改进边界框回归.
主要成果:
- 波兰YOLO在自制无人机绝缘器缺陷数据集上实现了92.4%的最先进的mAP@0.5.
- 在精度方面表现优于YOLOv5n,YOLOv8n,YOLOv10n和YOLO11n.
- 在只有155万个参数和3.8 GFLOPs的条件下实现了卓越的准确性.
- 建立了一个新的帕雷托前线,以提高机载缺陷检测的准确性和效率.
结论:
- 波尔德-约洛为无人机的实时绝缘体缺陷检测提供了高效和准确的解决方案.
- 显示了自动化电力线路检查和其他空中视觉任务的巨大潜力.
- 该模型的轻量级设计使其适用于资源有限的无人机平台.
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