Comprehensive Performance Evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on Object Detection of Power Equipment

TL;DR

The paper tackles the problem of reliable, real-time power equipment object detection to enhance power system reliability. It conducts a comprehensive evaluation of five YOLO variants (v5, v8, v9, v10, v11) on a power-equipment dataset, highlighting YOLOv11's architectural innovations (C3K2 CSP bottleneck, C2PSA attention, adaptive anchors) and a composite loss formulation. The study reports that YOLOv11 achieves the best overall performance, with a mAP of 57.2% and recall of 64.8%, outperforming earlier YOLO versions and demonstrating improved false-detection reduction. Grad-CAM visualizations corroborate the model’s focused attention on power equipment regions, underscoring its applicability for improving the operational reliability of power systems in industrial settings.

Abstract

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.

Figures (4)

• Figure 1: Overall architecture of YOLOv11 network.
• Figure 2: The power equipment label of two categories: wires and transformers.
• Figure 3: The experimental results visualization of YOLO series.
• Figure 4: The experiment heat map visualization of YOLO series.