Defect detection method of communication optical cable based on adaptive feature extraction

doi:10.11996/JG.j.2095-302X.2025020241

Abstract

Abstract:

With the expansion of communication line coverage, the traditional inspection method for electrical corrosion defects in all dielectric self-supporting (ADSS) optical cables have faced issues of low efficiency and high costs. To address these issues, a detection method for electrical corrosion defects in ADSS communication cables based on adaptive feature extraction was proposed. This method achieved detection of electrical corrosion defects in ADSS cables by making targeted improvements to the YOLOv8n model. Firstly, an ADown downsampling module was introduced into the backbone network to preserve more detailed information about the cable during the downsampling process. Subsequently, a context feature enhancement module was introduced, enabling the algorithm to learn the defect features of optical cables more specifically. Finally, a C2f_DSC module based on feature adaptive extraction was proposed, utilizing the dynamic serpentine convolution feature in the neck network to enhance the extraction of cable area features. Experiments conducted on an ADSS cable electrical corrosion dataset demonstrated that compared to the baseline model YOLOv8n, the proposed algorithm achieved a 2.5% improvement in mAP50 accuracy and a 2.2% increase in mAP50∶95 accuracy, providing a new and effective method for ADSS cable inspection.

Key words: ADSS optic cable, electrical corrosion, defect detection, feature adaptive extraction, YOLO

CLC Number:

TP391
TN913

WANG Zhidong, CHEN Chenyang, LIU Xiaoming. Defect detection method of communication optical cable based on adaptive feature extraction[J]. Journal of Graphics, 2025, 46(2): 241-248.

Figures/Tables 10

References 17

[1]	陈少磊, 罗洋, 邓平, 等. ADSS光缆电腐蚀机理研究及防范措施应用[J]. 电力信息与通信技术, 2019, 17(9): 67-73.
	CHEN S L, LUO Y, DENG P, et al. Research on electro-corrosion mechanism of ADSs optical cable and application of preventive measures[J]. Electric Power Information and Communication Technology, 2019, 17(9): 67-73 (in Chinese).
[2]	王富. ADSS光缆电腐蚀故障的在线监测措施[J]. 电子世界, 2018(14): 171, 173.
	WANG F. Online monitoring measures for galvanic corrosion faults in ADSS fiber optic cables[J]. Electronics World, 2018(14): 171, 173 (in Chinese).
[3]	刘传洋, 吴一全. 基于深度学习的输电线路视觉检测方法研究进展[J]. 中国电机工程学报, 2023, 43(19): 7423-7445.
	LIU C Y, WU Y Q. Research progress of vision detection methods based on deep learning for transmission lines[J]. Proceedings of the CSEE, 2023, 43(19): 7423-7445 (in Chinese).
[4]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2014: 580-587.
[5]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]// The 14th European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[6]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2016: 779-788.
[7]	裴少通, 张行远, 胡晨龙, 等. 基于ER-YOLO算法的跨环境输电线路缺陷识别方法[J]. 电工技术学报, 2024, 39(9): 2825-2840.
	PEI S T, ZHANG H X, HU C L, et al. The defect detection method for cross-environment power transmission line based on ER-YOLO algorithm[J]. Transactions of China Electrotechnical Society, 2024, 39(9): 2825-2840 (in Chinese).
[8]	王道累, 康博, 朱瑞. 基于深度学习的电力设备铭牌文本检测方法[J]. 图学学报, 2023, 44(4): 691-698. DOI
	WANG D L, KANG B, ZHU R. Text detection method for electrical equipment nameplates based on deep learning[J]. Journal of Graphics, 2023, 44(4): 691-698 (in Chinese).
[9]	WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. New York: IEEE Press, 2020: 1571-1580.
[10]	LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2018: 8759-8768.
[11]	LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2017: 936-944.
[12]	WANG C Y, YEH I H, LIAO H Y M. YOLOv9:learning what you want to learn using programmable gradient information[EB/OL]. [2024-06-13]. https://arxiv.org/abs/2402.13616.
[13]	CAI X H, LAI Q X, WANG Y W, et al. Poly kernel inception network for remote sensing detection[C]// 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2024: 27706-27716.
[14]	QI Y L, HE Y T, QI X M, et al. Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation[C]// 2023 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2023: 6047-6056.
[15]	ZHAO Y A, LV W Y, XU S L, et al. DETRs beat YOLOs on real-time object detection[C]// 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2024: 16965-16974.
[16]	曹义亲, 伍铭林, 徐露. 基于改进YOLOv5算法的钢材表面缺陷检测[J]. 图学学报, 2023, 44(2): 335-345. DOI
	CAO Y Q, WU M L, XU L. Steel surface defect detection based on improved YOLOv5 algorithm[J]. Journal of Graphics, 2023, 44(2): 335-345 (in Chinese).
[17]	WANG A, CHEN H, LIU L H, et al. YOLOv10:Real-time end-to-end object detection[EB/OL]. [2024-06-13]. https://arxiv.org/abs/2405.14458.

模型	P/%	R/%	mAP50/%	mAP50∶95/%	Params/M	GFLOPs
YOLOv8n	84.6	80.2	85.7	49.7	3.01	8.2
YOLOv8s+A	87.3	82.3	86.9	50.6	2.61	7.7
YOLOv8s+A+B	88.4	81.3	87.4	50.8	2.75	7.8
YOLOv8s+A+B+C	88.8	82.6	88.2	51.9	3.17	8.4

模型	P/%	R/%	mAP50/%	mAP50∶95/%	Params/M	GFLOPs
YOLOv8n	84.6	80.2	85.7	49.7	3.01	8.2
YOLOv8s+A	87.3	82.3	86.9	50.6	2.61	7.7
YOLOv8s+A+B	88.4	81.3	87.4	50.8	2.75	7.8
YOLOv8s+A+B+C	88.8	82.6	88.2	51.9	3.17	8.4

模型	mAP50/%	mAP50∶95/%	Params/M	GFLOPs	Time/ms	mAP50/%
模型	mAP50/%	mAP50∶95/%	Params/M	GFLOPs	Time/ms	断缆	击穿	坑蚀	电痕	局部坑蚀
SSD	77.1	33.3	24.28	30.63	31.40	89.8	75.2	69.3	73.4	77.9
Faster R-CNN	81.6	37.6	60.36	247.81	38.50	92.1	69.8	75.4	84.3	86.3
YOLOv3-tiny	84.3	45.9	12.13	19.00	1.86	92.6	88.6	77.4	79.6	83.3
YOLOv5n	85.1	48.3	2.51	7.20	4.66	92.2	92.5	74.8	79.2	86.7
YOLOv6n	85.1	49.5	4.24	11.90	3.84	89.4	88.5	81.7	79.6	86.0
YOLOv7-tiny	85.2	45.7	6.03	13.20	4.46	94.3	91.2	77.8	79.7	83.2
RT-DETR-R18	87.7	51.6	20.08	58.30	22.39	93.1	91.8	81.2	84.6	87.6
YOLOv10n	83.0	47.0	2.30	6.70	3.62	94.3	87.6	74.4	77.3	81.2
YOLOv8n	85.7	49.7	3.01	8.20	4.31	92.9	91.4	76.6	81.0	86.5
Ours	88.2	51.9	3.17	8.40	5.09	96.3	92.4	83.3	83.1	86.2

模型	mAP50/%	mAP50∶95/%	Params/M	GFLOPs	Time/ms	mAP50/%
模型	mAP50/%	mAP50∶95/%	Params/M	GFLOPs	Time/ms	断缆	击穿	坑蚀	电痕	局部坑蚀
SSD	77.1	33.3	24.28	30.63	31.40	89.8	75.2	69.3	73.4	77.9
Faster R-CNN	81.6	37.6	60.36	247.81	38.50	92.1	69.8	75.4	84.3	86.3
YOLOv3-tiny	84.3	45.9	12.13	19.00	1.86	92.6	88.6	77.4	79.6	83.3
YOLOv5n	85.1	48.3	2.51	7.20	4.66	92.2	92.5	74.8	79.2	86.7
YOLOv6n	85.1	49.5	4.24	11.90	3.84	89.4	88.5	81.7	79.6	86.0
YOLOv7-tiny	85.2	45.7	6.03	13.20	4.46	94.3	91.2	77.8	79.7	83.2
RT-DETR-R18	87.7	51.6	20.08	58.30	22.39	93.1	91.8	81.2	84.6	87.6
YOLOv10n	83.0	47.0	2.30	6.70	3.62	94.3	87.6	74.4	77.3	81.2
YOLOv8n	85.7	49.7	3.01	8.20	4.31	92.9	91.4	76.6	81.0	86.5
Ours	88.2	51.9	3.17	8.40	5.09	96.3	92.4	83.3	83.1	86.2