改进YOLOv5算法的变电站仪表目标检测方法

doi:10.11996/JG.j.2095-302X.2023030448

图学学报 ›› 2023, Vol. 44 ›› Issue (3): 448-455.DOI: 10.11996/JG.j.2095-302X.2023030448

• 图像处理与计算机视觉 • 上一篇下一篇

改进YOLOv5算法的变电站仪表目标检测方法

毛爱坤(), 刘昕明(), 陈文壮, 宋绍楼

辽宁工程技术大学电气与控制工程学院，辽宁葫芦岛 125105

收稿日期:2022-08-20 接受日期:2023-01-04 出版日期:2023-06-30 发布日期:2023-06-30
通讯作者: 刘昕明(1984-)，男，讲师，博士。主要研究方向为复杂工业建模、优化和诊断。E-mail：83660832@qq.com<
作者简介:
毛爱坤(1997-)，男，硕士研究生，主要研究方向为电力机器视觉深度学习。E-mail：2398494063@qq.com
基金资助:
辽宁省教育厅科学研究基金项目(LJY013)

Improved substation instrument target detection method for YOLOv5 algorithm

MAO Ai-kun(), LIU Xin-ming(), CHEN Wen-zhuang, SONG Shao-lou

Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao Liaoning 125105, China

Received:2022-08-20 Accepted:2023-01-04 Online:2023-06-30 Published:2023-06-30
Contact: LIU Xin-ming (1984-), lecturer, Ph.D. His main research interests cover complex industrial modeling, optimization and diagnosis. E-mail：83660832@qq.com
About author:
MAO Ai-kun (1997-), master student, His main research interest covesr deep learning of power machine vision. E-mail：2398494063@qq.com
Supported by:
Scientific Research Fund project of Liaoning Provincial Department of Education(LJY013)

摘要/Abstract

摘要：

高效、准确的边端仪表检测设备是构建智能变电站的重要环节。针对变电站的复杂环境，移动边端设备难以快速、准确地检测出小目标、多类别、高相似的仪表目标的问题，提出一种基于轻量级SS-YOLOv5网络的电力仪表目标检测方法。该算法以YOLOv5为基础，采用轻量级网络ShuffleNet V2改进模型网络结构，引入深度可分离卷积提取仪表特征，降低颈部融合时模型计算复杂度，提高检测速度；结合Swin Transformer通过移位窗口进行建模，实现全局和局部信息交互，提升特征提取能力；最后自主构建变电站仪表图像数据集对模型进行训练、测试和验证。实验结果表明，与原YOLOv5算法相比，对于检测压力表、电流表、电压表等仪表图像，模型参数量减少了91.8%，目标检测速度提升43.8%，为部署到边端提供一种可行的技术方案，能够推动变电站的信息化与智能化的建设。

关键词: 深度学习, 变电站仪表, YOLOv5, 目标检测, 轻量级网络

Abstract:

An efficient and accurate edge instrument detection equipment is crucial for building intelligent substations. However, due to the complex environment of substation, it is challenging for mobile side devices to quickly and accurately detect small targets, multi-categories, and highly similar instrument targets. To address this, a power instrument target detection method based on lightweight SS-YOLOv5 network was proposed. The algorithm was built on YOLOv5 and utilized the lightweight network ShuffleNet V2 to improve the model’s network structure. Deep separable convolutions were introduced to extract instrument features, reducing the computational complexity of the model during neck fusion and improving detection speed. Additionally, combined with Swin Transformer, modeling was undertaken through shift window, allowing for global and local information interaction and enhancing feature extraction ability. Finally, the image data set of substation instrument was constructed independently to train, test, and verify the model. The experimental results demonstrated that, compared with the YOLOv5 algorithm, the model parameters were reduced by 91.8% and the target detection speed was increased by 43.8% for the detection of pressure gauge, ammeter, voltmeter, and other instrument images. It provided a feasible technical scheme for deployment to the side, and could accelerate the development of substation informatization and intelligence.

Key words: deep learning, substation instruments, YOLOv5, target detection, lightweight network

中图分类号:

TP391

毛爱坤, 刘昕明, 陈文壮, 宋绍楼. 改进YOLOv5算法的变电站仪表目标检测方法[J]. 图学学报, 2023, 44(3): 448-455.

MAO Ai-kun, LIU Xin-ming, CHEN Wen-zhuang, SONG Shao-lou. Improved substation instrument target detection method for YOLOv5 algorithm[J]. Journal of Graphics, 2023, 44(3): 448-455.

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层数	网络层	输入尺寸	步长	通道数
0	CBLM	640×640×3	-	16
1	ShuffleLayer	160×160×16	2	64
2	C3SWinTR	80×80×64	-	64
3	ShuffleLayer	80×80×64	2	120
4	C3	40×40×120	-	120
5	ShuffleLayer	40×40×120	2	232
6	C3	20×20×232	-	232
7	DWConv	20×20×232	1	64
8	Upsample	20×20×64	-	-
9	Concat	-	-	-
10	ShuffleLayer	40×40×184	1	184
11	DWConv	40×40×184	1	32
12	Upsample	40×40×32	-	-
13	Concat	-	-	-
14	ShuffleLayer	80×80×96	1	96
15	Conv	80×80×96	2	32
16	Concat	-	-	-
17	ShuffleLayer	40×40×64	1	64
18	Conv	40×40×64	2	64
19	Concat	-	-	-
20	ShuffleLayer	20×20×128	1	128
21	Detect	-	-	-

层数	网络层	输入尺寸	步长	通道数
0	CBLM	640×640×3	-	16
1	ShuffleLayer	160×160×16	2	64
2	C3SWinTR	80×80×64	-	64
3	ShuffleLayer	80×80×64	2	120
4	C3	40×40×120	-	120
5	ShuffleLayer	40×40×120	2	232
6	C3	20×20×232	-	232
7	DWConv	20×20×232	1	64
8	Upsample	20×20×64	-	-
9	Concat	-	-	-
10	ShuffleLayer	40×40×184	1	184
11	DWConv	40×40×184	1	32
12	Upsample	40×40×32	-	-
13	Concat	-	-	-
14	ShuffleLayer	80×80×96	1	96
15	Conv	80×80×96	2	32
16	Concat	-	-	-
17	ShuffleLayer	40×40×64	1	64
18	Conv	40×40×64	2	64
19	Concat	-	-	-
20	ShuffleLayer	20×20×128	1	128
21	Detect	-	-	-

实验平台	配置
操作系统	Ubuntu 18.04
CPU	Intel@Xeon (R) Gold 6230
GPU	NVIDIA Ge Force RTX2080Ti
编程语言	Python 3.7
深度学习框架	Pytorch

实验平台	配置
操作系统	Ubuntu 18.04
CPU	Intel@Xeon (R) Gold 6230
GPU	NVIDIA Ge Force RTX2080Ti
编程语言	Python 3.7
深度学习框架	Pytorch

检测方案	mAP (%)	模型大小(MB)	GFLOGPs	检测速度(ms)	参数量
方案一	91.00	66.30	9.70	9.3	8 679 120
方案二	82.14	22.50	9.60	7.2	6 056 606
方案三	92.30	5.70	7.10	6.5	656 496
方案四	92.80	2.50	3.54	4.7	537 865
方案五	95.60	13.70	16.40	8.9	7 064 698
方案六	91.80	1.27	1.50	3.7	571 094
本文	94.40	1.32	1.70	5.0	575 198

改进YOLOv5算法的变电站仪表目标检测方法

Improved substation instrument target detection method for YOLOv5 algorithm

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图/表 14

参考文献 25

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序号	模型	mAP (%)	参数量
1	YOLOv5	95.6	7 064 698
2	YOLOv5+Shuffle	90.2	573 255
3	YOLOv5+Shuffle +深度可分离卷积	91.8	571 094
4	YOLOv5+Shuffle+ 深度可分离卷积+ Swin Transformer	94.4	575 198

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