Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former

doi:10.11930/j.issn.1004-9649.202305035

Abstract

Abstract:

The transmission lines are complex in distribution and it is difficult to effectively detect their faults. Among them, the connecting fittings are susceptible to corrosion and other faults due to their long exposure to complex environments. Aiming at the problem that the transmission line connection fitting components are varied in scale and have poor accuracy in detecting their corrosion faults, a detection method is proposed for transmission line connection fittings and their corrosion faults based on dual attention embedding reconstruction and Swin Transformer, i.e., PCSA-YOLOv7 Former. The experimental results show that the proposed method is superior to 12 existing state-of-the-art object detection algorithms in comprehensive detection performance of the constructed TLCF dataset, with the mAP_0.5 of the test set reaching 94.9 %. Compared with the baseline model YOLOv7, the proposed method improves the indexes F1 and mAP_0.5 by 2.6 percentage points and 2.2 percentage points, respectively, indicating that the proposed method can more comprehensively understand the multi-scale semantic information in the images of transmission line connection fittings and learn their subtle details that are difficult to distinguish.

Key words: transmission line connection fittings, PCSA-YOLOv7 Former, dual attention embedding, Swin Transformer, atrous spatial pyramid pooling

Zhiwei SONG, Xinbo HUANG, Chao JI, Fan ZHANG, Ye ZHANG. Transmission Line Connection Fittings and Corrosion Detection Method Based on PCSA-YOLOv7 Former[J]. Electric Power, 2024, 57(6): 141-152.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202305035

https://www.electricpower.com.cn/EN/Y2024/V57/I6/141

Figures/Tables 15

References 25

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类别	P	R	F₁	mAP_0.5
U形挂环 (US)	0.962	0.867	0.912	0.938
三角联板 (TYP)	0.936	0.989	0.962	0.993
调整板 (ADP)	0.867	0.932	0.898	0.963
耐张线夹 (TEC)	0.785	0.836	0.811	0.828
紧固螺栓 (CLB)	0.855	0.967	0.908	0.968
绝缘子 (In)	0.998	1.000	0.998	0.996
所有 (All)	0.902	0.932	0.917	0.949

类别	P	R	F₁	mAP_0.5
U形挂环 (US)	0.962	0.867	0.912	0.938
三角联板 (TYP)	0.936	0.989	0.962	0.993
调整板 (ADP)	0.867	0.932	0.898	0.963
耐张线夹 (TEC)	0.785	0.836	0.811	0.828
紧固螺栓 (CLB)	0.855	0.967	0.908	0.968
绝缘子 (In)	0.998	1.000	0.998	0.996
所有 (All)	0.902	0.932	0.917	0.949

场景	原始图片	TYP	In	ADP	TEC	CLB	US
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3
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场景	原始图片	TYP	In	ADP	TEC	CLB	US
1
2
3
4

算法	P/%	R/%	F₁/%	mAP_0.5/ %	GFLOPS/ G	Params/ M	FPS
Faster R- CNN	61.35	88.26	72.38	77.71	370.2	137.10	27.3
SSD	82.34	76.01	79.05	74.45	62.7	26.30	119.8
YOLOv3	91.62	60.77	73.07	82.76	66.2	61.90	63.5
YOLOv5	92.13	87.24	89.62	89.90	115.9	47.00	60.3
YOLOv5 Swin+ BiFPN	87.10	88.83	87.96	90.80	225.2	102.30	19.8
YOLOv5 Ghost backbone	90.20	92.10	91.14	93.90	42.2	24.20	44.8
FCOS	85.10	82.76	83.91	87.33	161.8	32.20	45.7
RetinaNet	91.10	76.00	82.87	77.85	170.1	37.90	44.3
CenterNet	98.80	63.26	77.13	87.18	70.2	32.70	88.4
TPH YOLOv5	88.11	89.60	88.85	92.50	270.3	112.90	16.2
YOLOX	89.20	92.29	90.72	92.40	26.6	8.94	60.1
YOLOv7	87.60	90.60	89.07	92.72	106.4	37.60	51.3
本文	90.23	93.20	91.69	94.90	114.3	41.40	47.1