[1]廖晓辉,谢子晨,辛忠良,等.基于轻量化YOLOv5的电气设备外部缺陷检测[J].郑州大学学报(工学版),2024,45(04):117-124.[doi:10.13705/ j.issn.1671-6833.2024.04.010]
　LIAO Xiaohui,XIE Zichen,XIN Zhongliang,et al.Electrical Equipment External Defect Detection Based on Lightweight YOLOv5[J].Journal of Zhengzhou University (Engineering Science),2024,45(04):117-124.[doi:10.13705/ j.issn.1671-6833.2024.04.010]
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基于轻量化YOLOv5的电气设备外部缺陷检测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年04期
页码:
117-124
栏目:
出版日期:
2024-06-16

文章信息/Info

Title:
Electrical Equipment External Defect Detection Based on Lightweight YOLOv5
文章编号:
1671-6833(2024)04-0117-08
作者:
廖晓辉1;  谢子晨1;  辛忠良2;  陈 怡1;  叶梁劲1
1.郑州大学 电气与信息工程学院,河南 郑州 450001;2.国网郑州供电公司,河南 郑州 450007
Author(s):
LIAO Xiaohui1;  XIE Zichen1;  XIN Zhongliang2;  CHEN Yi1;  YE Liangjin1
1.School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2.State Grid Zhengzhou Pow er Supply Company, Zhengzhou 450007, China
关键词:
缺陷检测;  电气设备;  轻量化YOLOv5;  EfficientViT网络;  SimAM注意力;  Soft-NMS结构
Keywords:
defect detection;  electrical equipment;  lightweight YOLOv5;  EfficientViT network;  SimAM attention;  Soft-NMS structure
分类号:
TP391.4TM63
DOI:
10.13705/ j.issn.1671-6833.2024.04.010
文献标志码:
A
摘要:
为了提高变电站电气设备外部缺陷实时检测的精度,同时让检测模型更加轻量化,提出了一种基于轻量 化YOLOv5的电气设备外部缺陷检测方法。首先,构建电气设备外部缺陷图像数据集并进行数据增强处理。其 次,采用3种优化策略对原YOLOv5进行改进:通过引入EfficientViT网络改进算法主干网络,减少模型参数量,并 在算法Neck部分中加入SimAM无参数注意力机制来提高变电站复杂背景下的识别精度,同时采用Soft-NMS模块 来改进检测框筛选方式,避免出现缺陷漏检现象。最后,通过消融实验进行验证。结果表明:轻量化后的电气设备 外部缺陷检测模型mAP值稳定在86.4%,与原模型相比提高了1.2百分点,模型参数量减少了20%,计算量减少了 38%,模型大小为11 MB,比原模型减少了19.7%。改进后的模型能够满足设备外部缺陷实时检测的要求,可以实 现模型的轻量化部署。
Abstract:
In order to improve the accuracy of real-time detection of external defects of electrical equipment in sub stations and make the detection model more lightweight, a lightweight YOLOv5 based external defect detection method for electrical equipment was proposed. Firstly, the external defect image dataset of electrical equipment was constructed and processed by data enhancement. Secondly, three optimization strategies were used to improve the original YOLOv5. The EfficientViT network was introduced to improve the backbone network of the algorithm to re duce the number of model parameters, and the SimAM parameter-free attention mechanism was added to the Neck part of the algorithm to improve the recognition accuracy with the complex background of the substation. At the same time, the Soft-NMS module was used to improve the screening method of the detection box to avoid the phe nomenon of defect missed detection. Finally, verified by ablation test, the mAP value of the lightweight external de fect detection model of electrical equipment was stable at 86.4%, which was 1.2 percentage points higher than that of the original model, the number of model parameters were reduced by 20%, the calculation amount was reduced by 38%, and the model size was 11 MB, which was 19.7% lower than that of the original model. The improved model could meet the requirements of real-time detection of external defects of equipment.

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更新日期/Last Update: 2024-06-14