A lightweight detection method for defects of transmission line insulators and accessories based on improved YOLOv11n

Abstract

Insulators and their accessories are core components ensuring the safe operation of transmission lines. Defect detection for them is confronted with challenges such as small targets, complex backgrounds and difficulty in fitting the irregular boundaries of defects, while traditional lightweight algorithms suffer from insufficient detection performance. To tackle these challenges, this work presents a lightweight detection algorithm built upon the improved YOLOv11n: the DCNv3 (Deformable Convolution v3) is embedded into the backbone network to replace the traditional convolutional layers, enhancing the ability to extract features of irregular defects; the Inner-WIoU(Inner-Weighted Intersection over Union) loss function is employed to refine the bounding box regression. All experiments are conducted on a self-built dataset with 7,972 images and 7 defect categories. The proposed method achieves a mAP@50 of 86.4%, 3.2 percentage points higher than the baseline YOLOv11n, and outperforms mainstream lightweight detectors and small-object-oriented detectors. Meanwhile, the algorithm retains the lightweight advantage and meets the needs of on-site real-time inspection. The model maintains a lightweight structure with only 2.6 M parameters and achieves an inference speed of 38 FPS on NVIDIA RTX 4080 Super, meeting real-time inspection requirements. Meanwhile, the algorithm retains the lightweight advantage and can meet the requirements of on-site real-time inspection. Since validation is limited to a self-constructed dataset, cross-scene testing and public benchmark evaluation will be conducted in future work to further verify the model’s robustness and generalization in real transmission-line inspection scenarios.