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| Self-Explosion Defect Detection of Power Insulators Based on Embedded YOLO Network |
| Li Peng1, Su Yunlong1, Ning Hao1, Meng Qingwei1, Wei Qiang2 |
1. College of New Energy China University of Petroleum (East China) Qingdao 266580 China;
2. State Grid Xinjiang Electric Power Co. Ltd Urumqi 830018 China |
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Abstract Insulators are critical insulating electrical components in power systems, and their proper insulating condition is essential to ensure the safe and stable operation of the power grid. To address the challenges of large model parameters and high computational complexity in existing insulator self-explosion fault detection models, which hinder their efficient deployment on resource-constrained embedded devices, this paper proposed a fault detection method for power insulators based on an embedded YOLO network.
First, a lightweight YOLO insulator defective target detection network based on depth-separable convolution, GhostNet network and SimSPPF spatial pyramid pooling weights was designed, which significantly reduces the number of parameters and computational complexity of the model while maintaining the detection accuracy. Meanwhile, the redundant parts in the network structure were effectively trimmed by the weight pruning method based on the L1 paradigm, which further compresses the model size. To compensate for the degradation in detection performance due to pruning, the features of the high-precision teacher model were migrated to the lightweight student model using knowledge distillation to improve the detection accuracy and performance of the pruned model.
The proposed lightweight YOLOv5s model was applied to a self-constructed insulator self-explosion defect detection dataset, achieving a significant improvement in detection speed and a notable reduction in model complexity, with only a slight decrease in overall average precision. When applied to another independent test set, the model also demonstrated strong generalization ability in detection. The model was designed to balance detection speed and accuracy, ensuring that it meets the memory and computational requirements of embedded devices. Additionally, the optimized model was deployed on an embedded device powered by the Rexchip RK3588, leveraging its computational performance to accelerate the inference process of the optimized model. Test results demonstrated that the deployed model could accurately detect insulator self-explosion defects at a real-time inference speed of 85 frames per second, which is 1.89 times faster than the original model. This significant improvement in efficiency demonstrates the effectiveness of the optimized model in detecting insulator self-explosion defects.
The following conclusions can be drawn from the experiments: (1) The designed lightweight insulator self-explosion defect detection model meets the memory requirements for embedded deployment and the computational performance requirements for real-time self-explosion defect detection, with minimal loss in detection performance. (2) Knowledge distillation technology compensates for the detection performance degradation caused by model lightweighting, achieving a good balance between detection speed and accuracy, while It also demonstrates strong generalization ability and robustness on the independent test set. (3) The model was deployed on the RK3588 embedded device, fully leveraging its computational performance to accelerate the efficient inference of the model. The lightweight model enables efficient detection of insulator self-explosion defects on embedded devices, contributing to improved insulator inspection efficiency in practical applications.
The dataset will be expanded in the future to include additional defect types, thereby enhancing the model's capacity to classify insulators with diverse defect types. Concurrently, the embedded device implemented in this study will be integrated with drones to enable real-time, on-site intelligent insulator inspection applications at the edge.
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Received: 26 November 2024
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2025, Vol. 40 
