基于模型压缩与知识蒸馏的绝缘子自爆缺陷轻量化实时检测网络

杨宏宽; 李作庆; 叶升言; 万顺昌

doi:10.13296/j.1001-1609.hva.2026.10.013

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基于模型压缩与知识蒸馏的绝缘子自爆缺陷轻量化实时检测网络

研究与分析 | 更新时间：2026-10-14

- 基于模型压缩与知识蒸馏的绝缘子自爆缺陷轻量化实时检测网络
- Lightweight Real-time Detection Network for Insulator Self-explosion Defects Based on Model Compression and Knowledge Distillation
- 高压电器 2026年62卷第10期页码：108-116
- 作者机构：
  
  云南电网有限责任公司德宏供电局，云南芒市 678400
- 作者简介：
  
  杨宏宽（1995—），男，硕士，助理工程师，主要从事电力设备智能运维技术方面的研究工作（通信作者）（E-mail：kustyhk@163.com）。
  李作庆（1993—），男，工程师，技师，从事电力系统继电保护、变电站智能化和数字化方面工作（E-mail：957619324@qq.com）。
  叶升言（1988—），男，工程师，从事配电生产技术管理工作（E-mail：387925430@qq.com）。
  万顺昌（1995—），男，助理工程师，技师，从事变电检修方面的工作（E-mail：1060319257@qq.com）。
- 基金信息：
  
  中国南方电网有限责任公司科技项目Science and Technology Project of China Southern Power Grid.(YNKJXM20220062)
- DOI：10.13296/j.1001-1609.hva.2026.10.013
  中图分类号：
- 收稿：2026-05-15，
  
  修回：2026-07-28，
  
  纸质出版：2026-10-16
- 稿件说明：
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杨宏宽, 李作庆, 叶升言, 等. 基于模型压缩与知识蒸馏的绝缘子自爆缺陷轻量化实时检测网络[J]. 高压电器, 2026,62(10):108-116.

YANG Hongkuan, LI Zuoqing, YE Shengyan, et al. Lightweight Real-time Detection Network for Insulator Self-explosion Defects Based on Model Compression and Knowledge Distillation[J]. High Voltage Apparatus, 2026, 62(10): 108-116.
杨宏宽, 李作庆, 叶升言, 等. 基于模型压缩与知识蒸馏的绝缘子自爆缺陷轻量化实时检测网络[J]. 高压电器, 2026,62(10):108-116. DOI： 10.13296/j.1001-1609.hva.2026.10.013.

YANG Hongkuan, LI Zuoqing, YE Shengyan, et al. Lightweight Real-time Detection Network for Insulator Self-explosion Defects Based on Model Compression and Knowledge Distillation[J]. High Voltage Apparatus, 2026, 62(10): 108-116. DOI： 10.13296/j.1001-1609.hva.2026.10.013.

摘要

针对目前绝缘子缺陷检测网络架构复杂、推理计算量大导致占用大量的储存空间、检测效率低下的问题，设计出一种绝缘子自爆缺陷轻量化实时检测网络（insulator defects net，IDNet）。首先，通过空间尺度变换的方法将YOLOv4-tiny基础网络中用于小目标检测任务的26像素×26像素的YOLOhead进行删除，降低网络复杂程度，提升检测速度；然后，根据预设的剪枝比例阈值进行卷积层通道剪枝来实现模型压缩，以减小网络权重，进一步提升网络检测速度；最后，采用知识蒸馏的方法将模型压缩时损失的精度做微量调控，恢复网络的准确性。实验结果表明，文中设计的IDNet绝缘子自爆缺陷轻量化实时检测网络较YOLOv4-tiny基础网络能保证在检测精度损失较少的前提下，网络权重压缩85.76%，降为3.19 MB；平均检测速度提升11.27%，达到44.02帧/s。文中为研制绝缘子自爆缺陷实时检测设备提供了检测速度快、准确性高且占用储存空间小的有效绝缘子自爆缺陷实时检测算法。

Abstract

In view of issues of present insulator such as complex network architecture and significant storage occupation as well as low detection efficiency due to large inference computation in defect detection

a lightweight realtime detection network for insulator selfe-explosion defects(insulator defects net

IDNet)is designed. First

the 26 pixel × 26 pixel YOLOhead in the YOLOv4-tiny base network

which is used for small object detection

is removed through spatial scale transformation

thereby reducingnetwork complexity and improving detection speed. Then

the channel pruning on the convolutional layer is performed in accordance with the preset pruning ratio threshold

thereby reducing network weights and further improving the detection speed. Finally

the knowledge distillation method is used to finely adjust the accuracy incurred during model compression

thereby restoring the network's accuracy.Experimental results show that

compared with the YOLOv4-tiny baseline network

the proposed IDNet lightweight real-time detection network for insulator self-explosion defects can achieve 85.76% compression in network weights(reduced to 3.19 MB)while incurring only a minor loss in detection accuracy. Moreover

the average detection speed is improved by 11.27%

reaching 44.02 frames per second. This paper provides an effective real-time detection algorithm for insulator self-explosion defects

which offers fast detection speed

high accuracy

and low storage space occupation

thereby faciliating the development of real-time detection equipment for insulator selfexplosion defects.

关键词

Keywords

references

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