基于嵌入式YOLO网络的电力绝缘子自爆缺陷检测

doi:10.19595/j.cnki.1000-6753.tces.242110

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摘要

针对现有绝缘子检测模型参数量和计算复杂度较高,难以在计算资源和存储资源有限的无人机等移动巡检设备上部署的问题,设计基于深度可分离卷积、Ghost Net网络和SimSPPF空间金字塔池化,重构了一种轻量化YOLOv5s绝缘子自爆缺陷检测网络,以降低模型参数量和模型复杂度;然后对轻量化模型采用结构化剪枝的方法对模型冗余的卷积层进行裁剪,实现模型进一步压缩;同时,通过知识蒸馏技术弥补模型剪枝造成的检测性能损失。将最终的轻量化模型在绝缘子自爆缺陷检测数据集上进行实验,结果表明,本文所提出的模型在保证检测精度的前提下,模型复杂度大大降低,推理检测速度明显提升。最后,将本文轻量化模型部署至RK3588为核心的嵌入式设备进行绝缘子自爆缺陷实时检测,测试结果显示本文提出的模型在嵌入式设备的实时推理帧率达到85帧,较YOLOv5s原模型的推理速度提升1.8倍,同时能够精确的检测出绝缘子的自爆缺陷部位,显著提高了绝缘子自爆缺陷检测的效率。

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关键词 ：绝缘子检测, 轻量化, 剪枝, 知识蒸馏, 嵌入式

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 1.8 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.

Key words： Insulator detection lightweight pruning knowledge distillation embedded

收稿日期: 2024-11-26

PACS:	TM216
	TP183

通讯作者: 宿雲龙男,2000年生,硕士研究生,研究方向为人工智能在电力系统故障检测中的实际应用。E-mail：yunlong_su0729@163.com

作者简介: 李鹏男,1987年生,副教授,硕士生导师,研究方向为机器学习和人工智能理论方法及其在电力系统和电气设备故障检测中的应用。E-mail：lipeng@upc.edu.cn

引用本文:

李鹏, 宿雲龙, 宁昊, 孟庆伟, 魏强. 基于嵌入式YOLO网络的电力绝缘子自爆缺陷检测[J]. 电工技术学报, 0, (): 2110-. Li Peng, Su Yunlong, Ning Hao, Meng Qingwei, Wei Qiang. Self-Explosion Defect Detection of Power Insulators Based on Embedded YOLO Network. Transactions of China Electrotechnical Society, 0, (): 2110-.

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