YOLOv11在电力设备检测中表现最优，准确率高达57.2%。
Comprehensive Performance Evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on Object Detection of Power Equipment
- 对比五种YOLO系列模型，YOLOv11在电力设备检测中效果最佳。
- YOLOv11 mAP达57.2%，优于其他模型，且误检更少。
- 适合关注电力系统可靠性与目标检测性能的工程应用者。
随着全球工业生产的快速发展，电力设备的可靠性需求持续提升。保障电力系统稳定运行需精准检测设备潜在故障，以确保电能正常供应。本文全面评估了YOLOv5、YOLOv8、YOLOv9、YOLOv10及最新YOLOv11在电力设备目标检测中的表现。实验结果显示，这些模型在公开电力设备数据集上的平均精度均值（mAP）分别为54.4%、55.5%、43.8%、48.0%和57.2%，其中YOLOv11达到最高水平。此外，YOLOv11在召回率上表现更优，且显著降低了误检率。结论表明，YOLOv11为电力设备检测提供了可靠有效的解决方案，是提升电力系统运行可靠性的一项有前景技术。
原文摘要 · Abstract (English)
With the rapid development of global industrial production, the demand for reliability in power equipment has been continuously increasing. Ensuring the stability of power system operations requires accurate methods to detect potential faults in power equipment, thereby guaranteeing the normal supply of electrical energy. In this article, the performance of YOLOv5, YOLOv8, YOLOv9, YOLOv10, and the state-of-the-art YOLOv11 methods was comprehensively evaluated for power equipment object detection. Experimental results demonstrate that the mean average precision (mAP) on a public dataset for power equipment was 54.4%, 55.5%, 43.8%, 48.0%, and 57.2%, respectively, with the YOLOv11 achieving the highest detection performance. Moreover, the YOLOv11 outperformed other methods in terms of recall rate and exhibited superior performance in reducing false detections. In conclusion, the findings indicate that the YOLOv11 model provides a reliable and effective solution for power equipment object detection, representing a promising approach to enhancing the operational reliability of power systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。