TFD-YOLOv8：一种用于输电线路的异物检测方法

doi:10.11996/JG.j.2095-302X.2024050901

图学学报 ›› 2024, Vol. 45 ›› Issue (5): 901-912.DOI: 10.11996/JG.j.2095-302X.2024050901

• 图像处理与计算机视觉 • 上一篇下一篇

TFD-YOLOv8：一种用于输电线路的异物检测方法

王亚茹(), 冯利龙, 宋晓轲, 屈卓, 杨珂, 王乾铭(), 翟永杰

华北电力大学自动化系，河北保定 071003

收稿日期:2024-05-08 修回日期:2024-06-25 出版日期:2024-10-31 发布日期:2024-10-31
通讯作者:王乾铭(1995-)，男，讲师，博士。主要研究方向为电力视觉。E-mail：qianmingwang@ncepu.edu.cn
第一作者:王亚茹(1990-)，女，讲师，博士。主要研究方向为输电线路元件检测等。E-mail：wangyaru@ncepu.edu.cn
基金资助:
国家自然科学基金青年基金项目(62303184);国家自然科学基金联合基金项目重点支持项目(U21A20486);国家自然科学基金面上项目(62373151);中央高校基本科研业务费专项资金资助项目(2023JC006);中央高校基本科研业务费专项资金资助项目(2024MS136)

TFD-YOLOv8: a transmission line foreign object detection method

WANG Yaru(), FENG Lilong, SONG Xiaoke, QU Zhuo, YANG Ke, WANG Qianming(), ZHAI Yongjie

Department of Automation, North China Electric Power University, Baoding Hebei 071003, China

Received:2024-05-08 Revised:2024-06-25 Published:2024-10-31 Online:2024-10-31
Contact: WANG Qianming (1995-), lecturer, Ph.D. His main research interest covers power vision. E-mail：qianmingwang@ncepu.edu.cn
First author：WANG Yaru (1990-), lecturer, Ph.D. Her main research interests cover detection of transmission line components, etc. E-mail：wangyaru@ncepu.edu.cn
Supported by:
Youth Fund of the National Natural Science Foundation of China Project(62303184);National Natural Science Foundation of China Joint Fund Project Key Support Project(U21A20486);The National Nature Science Foundation of China(62373151);Fundamental Research Funds for the Central Universities(2023JC006);Fundamental Research Funds for the Central Universities(2024MS136)

摘要/Abstract

摘要：

基于无人机航拍图像的异物检测是输电线路智能巡检中的重要环节。YOLO目标检测算法精度高、速度快，是目前的主流算法。但在进行输电线路异物检测时，由于异物目标尺度多变、特征不显著，易出现误检、漏检等问题，提出一种用于输电线路异物检测的YOLOv8模型(TFD-YOLOv8)。首先，在YOLOv8颈部网络构建双分支下采样模块，截留下采样过程中易丢失的尺度相关细节信息，实现语义信息和细节信息的高效融合，提升不同尺度特征图的信息一致性。然后，在主干网络插入混合增强注意力模块，同时提取图像的全局和局部特征，分别生成空间注意力和通道注意力，得到一个包含局部信息、全局信息、空间信息和通道信息的混合增强注意力，增强网络对目标关键特征的捕捉能力。实验结果表明，与基线模型相比，本文方法的平均检测精度提升了6.7%，准确率和召回率分别提升了12.9%和5.1%，与多个现有目标检测方法相比，该方法在检测精度和复杂度上均具有优势。

关键词: YOLOv8, 输电线路, 异物检测, 下采样, 混合增强

Abstract:

Foreign object detection based on UAV aerial images is an important aspect for intelligent inspection of transmission lines. The YOLO target detection algorithm is high in accuracy and speed, making it the current mainstream algorithm. However, when carrying out transmission line foreign object detection, due to the variable scale and insignificant features of foreign object targets, problems such as misdetection and omission detection would arise. A YOLOv8 model (transmission line foreign detection-YOLOv8, TFD-YOLOv8) was proposed for transmission line foreign object detection. A two-branch downsampling module was constructed in the YOLOv8s neck network to intercept the scale-related detail information easily lost during the downsampling process, achieving the efficient fusion of semantic and detail information and improving the information consistency of feature maps at different scales. Then, a mix-enhancement attention module was inserted into the backbone network to simultaneously extract global and local features of the image, generating spatial attention and channel attention, respectively, and resulting in a mix-enhancement attention including local, global, spatial, and channel information. This enhanced the network’s ability to capture the key features of the targe. The experimental results showed that compared with the baseline model, the proposed method improved the average detection accuracy by 6.7%, and the accuracy and recall by 12.9% and 5.1%, respectively. This method demonstrated advantages in terms of detection accuracy and complexity compared with several existing target detection methods.

Key words: YOLOv8, transmission line, object detection, downsampling, mix-enhancement

中图分类号:

王亚茹, 冯利龙, 宋晓轲, 屈卓, 杨珂, 王乾铭, 翟永杰. TFD-YOLOv8：一种用于输电线路的异物检测方法[J]. 图学学报, 2024, 45(5): 901-912.

WANG Yaru, FENG Lilong, SONG Xiaoke, QU Zhuo, YANG Ke, WANG Qianming, ZHAI Yongjie. TFD-YOLOv8: a transmission line foreign object detection method[J]. Journal of Graphics, 2024, 45(5): 901-912.

图/表 13

图1 TFD-YOLOv8网络结构

Fig. 1 TFD-YOLOv8 network structure

图2 DBD模块结构

Fig. 2 DBD module structure

图3 MIX模块结构

Fig. 3 MIX module structure

表1 实验环境

Table 1 Experimental environment

硬件名称	型号
CPU	Intel(R) Core(TM) i9-10850K
GPU	GeForce RTX 3080
操作系统	Ubuntu 18.04
框架	Pytorch 1.12.0
计算架构	Cuda 11.3
语言	Python 3.10

图4 部分图像((a)大尺度鸟巢；(b)小尺度鸟巢；(c)鸟巢遮挡；(d)风筝；(e)垃圾；(f)气球)

Fig. 4 Partial images ((a) Large-scale nest; (b) Small-scale nest; (c) Obscured nest; (d) Kite; (e) Trash; (f) Balloon)

表2 不同模块的消融实验结果/%

Table 2 Results of ablation experiments/%

Method	P	R	AP50		mAP50	mAP50:95
Method	P	R	nest类	other类	mAP50	mAP50:95
YOLOv8s (基线模型)	78.4	57.8	69.5	63.5	66.5	33.0
YOLOv8s+MIX	84.9	61.8	71.9	69.1	70.5	35.1
YOLOv8s+DBD	89.3	61.4	70.3	69.1	69.7	34.2
Ours	91.3	62.9	73.0	73.3	73.2	35.8

图5 基线模型添加MIX模块前后的热力图对比场景((a)原图；(b)目标放大图；(c)基线模型；(d)基线模型+MIN)

Fig. 5 Comparison of thermograms before and after the addition of the MIX module to the baseline model ((a) Original drawing; (b) Zoom in on the target; (c) Baseline model; (d) Baseline model+MIX)

图6 基线模型添加DBD模块前后的下采样特征可视化对比((a)场景1；(b)场景2；(c)场景3；(d)场景4；(e)场景5)

Fig. 6 Comparison of downsampled feature visualizations before and after adding the DBD module to the baseline model ((a) Scenario 1; (b) Scenario 2; (c) Scenario 3; (d) Scenario 4; (e) Scenario 5)

表3 不同尺度目标的检测结果

Table 3 Experimental results for different scale targets

目标尺度	Method	P/%	R/%	AP50/%		mAP50/%	mAP50:95_/%
目标尺度	Method	P/%	R/%	nest类	other类	mAP50/%	mAP50:95_/%
小尺度	YOLOv8s	80.1	60.7	50.2	99.5	74.8	39.1
	YOLOv8s+MIX	85.7	70.2	54.1	99.5	76.8	40.7
	YOLOv8s+DBD	85.3	71.3	56.7	99.5	78.1	39.9
	Ours	86.1	70.5	61.2	99.5	80.3	42.3
中尺度	YOLOv8s	85.3	68.0	76.3	70.3	73.3	34.9
	YOLOv8s+MIX	85.9	65.4	80.4	73.2	76.8	35.8
	YOLOv8s+DBD	91.0	68.5	76.1	77.2	76.7	35.7
	Ours	95.0	68.6	76.5	75.1	75.8	34.2
大尺度	YOLOv8s	91.4	62.0	87.4	56.9	72.1	39.5
	YOLOv8s+MIX	89.9	65.2	86.7	64.9	75.8	42.3
	YOLOv8s+DBD	88.4	66.9	86.0	61.6	73.8	41.0
	Ours	91.4	65.8	87.5	69.5	78.5	43.5

图7 本文方法与基线模型检测可视化对比((a)误检1；(b)误检2；(c)误检3；(d)漏检1；(e)漏检2；(f)漏检3)

Fig. 7 Comparison of the methods with the baseline model detection visualization ((a) Misdetection 1; (b) Misdetection 2; (c) Misdetection 3; (d) Omission 1; (e) Omission 2; (f) Omission 3)

表4 不同注意力模块性能对比

Table 4 Comparison results of attention modules

Method	P/%	R/%	AP50/%		mAP50/%	参数量/M
Method	P/%	R/%	nest类	other类	mAP50/%	参数量/M
基线模型YOLOv8s	78.4	57.8	69.5	63.5	66.5	11.126 3
ECA^[26]	84.3	59.9	70.7	68.3	69.5	11.126 4
CA^[27]	82.3	60.4	70.6	67.0	69.1	11.159 0
EMA^[28]	83.5	60.7	70.0	67.3	68.7	11.159 8
SE^[29]	81.8	60.9	72.0	64.8	68.4	11.152 6
SA^[30]	82.0	61.4	70.7	67.7	69.2	11.126 8
GAM^[31]	83.7	59.4	71.6	66.9	69.2	16.380 6
MIX(Ours)	84.9	61.8	71.9	69.1	70.5	11.126 4

表5 不同方法的检测性能对比

Table 5 Comparison of different model performances

检测方法	mAP50 /%	mAP50:95 /%	参数 /M	权重 /MB
Faster R-CNN^[4]	67.8	27.8	41.35	315.64
Cascade R-CNN^[21]	67.7	28.9	69.15	528.83
DynamicR-CNN^[22]	64.5	27.4	41.75	319.31
TPH-YOLOv5^[23]	61.6	27.4	45.37	88.11
YOLOF^[24]	61.4	30.0	42.36	322.70
YOLOv7^[25]	67.6	32.5	36.48	74.84
YOLOv8s^[14]	66.5	33.0	11.12	21.48
Ours	73.2	35.8	13.48	26.04

图8 不同方法的检测结果对比((a)误检1；(b)误检2；(c)漏检1；(d)漏检2；(e)漏检3)

Fig. 8 Comparison of detection results using different methods ((a) Misdetection 1; (b) Misdetection 2; (c) Omission 1; (d) Omission 2; (e) Omission 3)

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TFD-YOLOv8：一种用于输电线路的异物检测方法

TFD-YOLOv8: a transmission line foreign object detection method

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