电力配电塔的完全自主实时缺陷检测:基于YOLOv11n的小型目标缺陷检测方法
Jingtao Zhang1, Siwen Chen1, Wei Wang2
1Department of Robotics Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Road, Pukou District, Nanjing 210044, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究介绍了TDD-YOLO,这是一个基于无人机的优化系统,用于自动化分配塔检查. 它显著提高了实时检测小缺陷的性能,提高了检查效率和安全性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 使用无人机自动化分配塔检查在实时缺陷检测方面面临挑战.
- 有限的计算能力和关键缺陷的小尺寸阻碍了当前的自动化系统.
研究的目的:
- 提出TDD-YOLO,这是一个优化的模型,用于在基于无人机的配送塔检查中增强小缺陷检测.
- 为了提高自动缺陷识别的准确性和实时性能.
主要方法:
- 开发了TDD-YOLO,这是一个基于YOLOv11n的优化模型,结合了SPD-Conv,CBAM,BiFPN和高分辨率检测头.
- 在定制数据集上对模型进行评估,以检测缺陷.
- 在Jetson Orin Nano上部署该系统进行实时性能测试.
主要成果:
- TDD-YOLO实现了0.873的平均平均精度 (mAP@0.5),比基线高出3.9%.
- 该系统在640×640分辨率的Jetson Orin Nano上演示了28FPS的实际率.
- 提议的改进有效地提高了小缺陷检测和定位精度.
结论:
- TDD-YOLO提供了一个可行的解决方案,用于实时,在配电塔检查中自动检测缺陷.
- 优化的模型显著提高了无人机在基础设施监控方面的能力.
- 该系统的性能表明,该系统可用于自主检查任务的实际应用.
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