The defect detection method for communication optical cables based on lightweight improved YOLOv8

doi:10.11996/JG.j.2095-302X.2025010028

Abstract

Abstract:

In the field of defect detection of all dielectric self supporting (ADSS) communication cables, the detection of galvanic corrosion defects across scales has the problems of high computational demands and low detection accuracy. In this paper, a defect detection method for ADSS communication cables with improved YOLOv8 was proposed. Firstly, the self-built communication cable defect dataset was sliced to prevent the existence of galvanic corrosion defects in the fiber optic cable from being lost in the process of scaling; secondly, the structure of LS-FPN replaced the traditional necking structure, retaining the favorable positional information in the channel dimension, which resolved the defective scale-spanning problem on the surface of the fiber optic cable while enhancing defect localization capability; furthermore, the idea of deformable convolution was introduced, replacing the convolution in the original backbone network, allowing the network to better focus on the surrounding defect information in the process of feature extraction; finally, the original CIoU was replaced by the Focus-MPDIoU loss function, which excels in handling boundary cases and avoids overly radical loss gradients. The experimental results validated that the method on the ADSS communication fiber optic cable defect dataset, with the improved model achieving 50.6% and 87.8% on mAP50-95 and mAP50, respectively, reflecting increases of 2.1% and 3.7% compared to YOLOv8n. Meanwhile, the computational GFLOPs were reduced to 6.8 and the number of parameters decreased to 1.96 M, reducing the configuration requirements of the inspection equipment and meeting the lightweight industrial demand.

Key words: lightweight, YOLOv8n, communication fiber optic cable, defect detection, MPDIoU

CLC Number:

WANG Zhidong, CHEN Chenyang, LIU Xiaoming. The defect detection method for communication optical cables based on lightweight improved YOLOv8[J]. Journal of Graphics, 2025, 46(1): 28-34.

Figures/Tables 12

References 17

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Model	mAP50/%	mAP50-95/%	GFLOPs	Params/M
YOLOv8n	84.1	48.5	8.1	3.01
YOLOv8s	88.4	52.3	28.4	11.14
YOLOv8m	90.1	55.1	78.7	25.86

Model	mAP50/%	mAP50-95/%	GFLOPs	Params/M
YOLOv8n	84.1	48.5	8.1	3.01
YOLOv8s	88.4	52.3	28.4	11.14
YOLOv8m	90.1	55.1	78.7	25.86

A	B	C	mAP50/%	mAP50-95/%	GFLOPs	Params/M	Speed/ms
Baseline			84.1	48.5	8.1	3.01	2.1
√			84.5	49.4	8.0	3.03	1.5
	√		86.0	49.7	6.9	1.93	1.8
		√	84.9	49.0	8.1	3.01	2.4
√	√		86.4	50.3	6.8	1.96	2.2
√		√	85.4	49.5	8.0	3.03	2.3
	√	√	85.9	50.3	6.9	1.93	1.6
√	√	√	87.8	50.6	6.8	1.96	2.1

A	B	C	mAP50/%	mAP50-95/%	GFLOPs	Params/M	Speed/ms
Baseline			84.1	48.5	8.1	3.01	2.1
√			84.5	49.4	8.0	3.03	1.5
	√		86.0	49.7	6.9	1.93	1.8
		√	84.9	49.0	8.1	3.01	2.4
√	√		86.4	50.3	6.8	1.96	2.2
√		√	85.4	49.5	8.0	3.03	2.3
	√	√	85.9	50.3	6.9	1.93	1.6
√	√	√	87.8	50.6	6.8	1.96	2.1

Model	mAP50/%	mAP50-95/%	GFLOPs	Params/M	FPS
YOLOv3-tiny	81.8	44.8	18.9	12.10	18.9
YOLOv5n	83.4	47.2	7.1	2.50	19.4
YOLOv6n	83.9	46.8	11.9	4.20	23.1
YOLOv8n	84.1	48.5	8.1	3.01	20.1
YOLOv9t	84.0	48.1	7.9	2.00	14.0
YOLOv10n	82.9	47.4	8.4	2.70	20.3
Gold-YOLO	84.6	48.2	10.2	5.90	15.9
RT-DETR	87.5	49.5	125.6	41.90	4.5
Ours	87.8	50.6	6.8	1.96	19.1