[1]廖晓辉,熊宗毅,孔　斌,等.基于数据增强与改进YOLOv5的电气设备红外图像检测[J].郑州大学学报(工学版),2026,47(02):77-84.[doi:10.13705/j.issn.1671-6833.2025.05.014]
　LIAO Xiaohui,XIONG Zongyi,KONG Bin,et al.Infrared Image Detection of Electrical Equipment Based on Data Enhancement and Improved YOLOv5[J].Journal of Zhengzhou University (Engineering Science),2026,47(02):77-84.[doi:10.13705/j.issn.1671-6833.2025.05.014]
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基于数据增强与改进YOLOv5的电气设备红外图像检测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年02期
页码:
77-84
栏目:
出版日期:
2026-02-13

文章信息/Info

Title:
Infrared Image Detection of Electrical Equipment Based on Data Enhancement and Improved YOLOv5
文章编号:
1671-6833(2026)02-0077-08
作者:
廖晓辉;  熊宗毅;  孔　斌;  谢子晨;  刘向阳;  郜子阳
郑州大学 电气与信息工程学院,河南 郑州 450001
Author(s):
LIAO Xiaohui;  XIONG Zongyi;  KONG Bin;  XIE Zichen;  LIU Xiangyang;  GAO Ziyang
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
电气设备;  数据增强;  GD机制;  Focal-CIoU损失函数;  红外图像识别
Keywords:
electrical equipment;  data augmentation;  GD mechanism;  Focal-CIoU loss function;  infrared image recognition
分类号:
TP391.4 TM63
DOI:
10.13705/j.issn.1671-6833.2025.05.014
文献标志码:
A
摘要:
为了使电气设备发热缺陷检测方法更加完善,提高算法对电气设备红外图像的识别精度,提出了基于数据增强与改进YOLOv5的电气设备红外图像检测方法。首先,针对电气设备红外图像信噪比和对比度低的问题,采用快速引导滤波算法对数据集中的红外图像进行去噪,通过引入Gamma校正来改进CLAHE算法,进而对红外图像进行对比度增强处理;其次,为了提高检测算法的精度,在原YOLOv5算法的基础上引入信息聚集和分发机制来改进特征融合模块,增强了多尺度特征融合能力,同时还引入了Focal-CIoU损失函数,使算法更加关注高质量样本,抑制低质量样本,提升了模型收敛速度;最后,经验证,改进后的算法在自建数据集上的mAP为93.6%,较改进前提高了4.0百分点。所提算法的mAP较Faster R-CNN、SSD、YOLOv3、YOLOv7 4种算法分别提高了4.5百分点、6.1百分点、4.7百分点和3.5百分点,且帧率达到32帧/s,可以满足对电气设备的实时识别要求。
Abstract:
In order to improve the detection method of heating defects of electrical equipment and the recognition accuracy of the algorithm for infrared images of electrical equipment, a method of infrared image detection of electrical equipment based on data enhancement and improved YOLOv5 was proposed. Firstly, for the problem of low signal-to-noise ratio and low contrast of infrared images of electrical equipment, a fast guided filtering algorithm was used to denoise the infrared images in the data set. The CLAHE algorithm was improved by introducing Gamma correction, and then the contrast of infrared images was enhanced. The premise of detecting the heating defects of electrical equipment was to accurately identify and classify the equipment. Then, in order to improve the accuracy of the detection algorithm, the information aggregation and distribution mechanism was introduced to improve the feature fusion module based on the original YOLOv5 algorithm, which enhanced the multi-scale feature fusion ability. Meanwhile, the Focal-CIoU loss function was also introduced to make the algorithm pay more attention to high-quality samples and suppress low-quality samples, which enhanced the rate of convergence for the model. It was verified that the mAP value of the improved algorithm on the self-built data set was 93.6%, which was 4.0 percentage points higher than that before the improvement. Compared with Faster R-CNN, SSD, YOLOv3 and YOLOv7, the mAP value of the proposed algorithm was increased by 4.5 percentage points, 6.1 percentage points, 4.7 percentage points and 3.5 percentage points respectively, and the frame rate reached 32 frames per second, which could meet the real-time recognition requirements of electrical equipment.

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更新日期/Last Update: 2026-03-04