YOLOLS: A Lightweight and High-Precision Power Insulator Defect Detection Network for Real-Time Edge Deployment

Qinglong Wang, Zhengyu Hu, Entuo Li, Guyu Wu, Wengang Yang, Yunjian Hu, Wen Peng and Jie Sun ()
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Qinglong Wang: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Zhengyu Hu: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Entuo Li: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Guyu Wu: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Wengang Yang: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China
Yunjian Hu: School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China
Wen Peng: The State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China
Jie Sun: The State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China

Energies, 2025, vol. 18, issue 7, 1-20

Abstract: Real-time insulator defect detection is critical for ensuring the reliability and safety of power transmission systems. However, deploying deep learning models on edge devices presents significant challenges due to limited computational resources and strict latency constraints. To address these issues, we propose YOLOLS, a lightweight and efficient detection model derived from YOLOv8n and optimized for real-time edge deployment. Specifically, YOLOLS integrates GhostConv to generate feature maps through stepwise convolution, reducing computational redundancy while preserving representational capacity. Moreover, the C2f module is restructured into a ResNet–RepConv architecture, in which convolution and Batch Normalization layers are fused during inference to reduce model complexity and enhance inference speed. To further optimize performance, a lightweight shared-convolution detection head significantly reduces parameter count and computational cost without compromising detection accuracy. Additionally, an auxiliary bounding box mechanism is incorporated into the CIoU loss function, improving both convergence speed and localization precision. Experimental validation on the CPLID dataset demonstrates that YOLOLS achieves a 42.4% reduction in parameters and a 48.1% decrease in FLOPs compared to YOLOv8n while maintaining a high mAP of 91%. Furthermore, when deployed on Jetson Orin NX, YOLOLS achieves 44.6 FPS, ensuring real-time processing capability. Compared to other lightweight YOLO variants, YOLOLS achieves a better balance between accuracy, computational efficiency, and inference speed, making it an optimal solution for real-time insulator defect detection in resource-constrained edge computing environments.

Keywords: lightweight detection; transmission line; power insulator defects; edge computing; real-time detection (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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