Vibration damper defect detection algorithm based on improved YOLOv8

doi:10.11996/JG.j.2095-302X.2025030532

Abstract

Abstract:

During drone inspections of transmission lines, the aerial images of vibration dampers exhibited varying target scales and complex backgrounds, which can easily lead to missed or false detections. To address the limitations of existing object detection algorithms in handling complex backgrounds and multi-scale target detection, an improved YOLOv8-based detection algorithm for identifying defects in vibration dampers was proposed. Firstly, to enhance the model’s ability to extract multi-scale features, a multi-scale feature extraction (MSFE) module was introduced, effectively expanding the model’s receptive field. Secondly, to suppress interference from complex backgrounds during the multi-scale feature fusion process, a space pyramid kernel attention (SPKA) module was designed to improve the model’s global awareness of the target. Lastly, to improve the detection capability for small target defects, a small target semantic information layer (STSIL) was added to the network, providing rich semantic information for small-scale targets that were easily overlooked in images. In the comparison experiments, the mAP⁵⁰ of the improved algorithm increased by 5.7% over the baseline model YOLOv8s, with AP⁵⁰ for normal, tilted, and fallen vibration dampers increasing by 3.4%, 4.5%, and 9.2%, respectively, demonstrating the effectiveness and superiority of the proposed algorithm in detecting defects in vibration dampers. The application of the proposed algorithm was expected to contribute to ensuring the safe and reliable operation of the power system.

Key words: transmission lines, vibration dampers, defect detection, multi-scale targets, feature fusion, attention mechanism

CLC Number:

NIU Hang, GE Xinyu, ZHAO Xiaoyu, YANG Ke, WANG Qianming, ZHAI Yongjie. Vibration damper defect detection algorithm based on improved YOLOv8[J]. Journal of Graphics, 2025, 46(3): 532-541.

Figures/Tables 13

References 26

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检测算法	AP⁵⁰			mAP⁵⁰	mAP^50:95
检测算法	正常	倾斜	掉落	mAP⁵⁰	mAP^50:95
TPH-YOLOv5	68.9	55.3	70.0	64.7	37.0
GBH-YOLOv5	69.0	56.7	66.4	64.0	35.5
YOLOv6s	65.1	56.5	71.3	64.3	35.9
YOlOv7-tiny	66.6	53.3	77.3	65.7	36.0
YOLOv8s	69.3	55.3	69.8	64.8	37.2
YOLOv10s	68.1	51.7	71.3	63.7	34.7
YOLO11s	67.5	52.8	67.7	62.6	34.8
Ours	72.7	59.8	79.0	70.5	39.1

检测算法	AP⁵⁰			mAP⁵⁰	mAP^50:95
检测算法	正常	倾斜	掉落	mAP⁵⁰	mAP^50:95
TPH-YOLOv5	68.9	55.3	70.0	64.7	37.0
GBH-YOLOv5	69.0	56.7	66.4	64.0	35.5
YOLOv6s	65.1	56.5	71.3	64.3	35.9
YOlOv7-tiny	66.6	53.3	77.3	65.7	36.0
YOLOv8s	69.3	55.3	69.8	64.8	37.2
YOLOv10s	68.1	51.7	71.3	63.7	34.7
YOLO11s	67.5	52.8	67.7	62.6	34.8
Ours	72.7	59.8	79.0	70.5	39.1

实验	MSFE	SPKA	STSIL	AP^50:95/%			mAP⁵⁰/%	mAP^50:95/%	Params/M	FPS/(帧/秒)
实验	MSFE	SPKA	STSIL	Small	Medium	Large	mAP⁵⁰/%	mAP^50:95/%	Params/M	FPS/(帧/秒)
1				34.5	48.6	30.0	63.1	35.8	11.2	185.2
2	√			35.5	50.9	43.5	64.7	35.2	10.0	212.8
3		√		35.5	51.2	35.0	65.5	37.7	12.3	153.8
4			√	36.7	46.2	25.0	65.8	37.9	11.4	172.4
5	√	√		35.1	52.8	42.5	66.4	37.6	11.5	178.8
6	√		√	37.4	49.6	35.7	66.1	37.1	10.9	196.0
7		√	√	35.2	51.2	35.0	66.7	37.2	11.7	163.9
8	√	√	√	37.6	51.6	49.0	69.7	38.9	10.8	179.6

实验	MSFE	SPKA	STSIL	AP^50:95/%			mAP⁵⁰/%	mAP^50:95/%	Params/M	FPS/(帧/秒)
实验	MSFE	SPKA	STSIL	Small	Medium	Large	mAP⁵⁰/%	mAP^50:95/%	Params/M	FPS/(帧/秒)
1				34.5	48.6	30.0	63.1	35.8	11.2	185.2
2	√			35.5	50.9	43.5	64.7	35.2	10.0	212.8
3		√		35.5	51.2	35.0	65.5	37.7	12.3	153.8
4			√	36.7	46.2	25.0	65.8	37.9	11.4	172.4
5	√	√		35.1	52.8	42.5	66.4	37.6	11.5	178.8
6	√		√	37.4	49.6	35.7	66.1	37.1	10.9	196.0
7		√	√	35.2	51.2	35.0	66.7	37.2	11.7	163.9
8	√	√	√	37.6	51.6	49.0	69.7	38.9	10.8	179.6

检测算法	AP⁵⁰				mAP⁵⁰
检测算法	立交桥	运动场	飞机	油桶	mAP⁵⁰
TPH-YOLOv5	93.1	99.2	94.4	97.5	96.1
GBH-YOLOv5	89.3	99.3	94.6	96.9	95.0
YOLOv6s	77.3	98.4	90.7	95.4	90.5
YOlOv7-tiny	94.0	98.8	94.3	96.9	96.0
YOLOv8s	76.2	99.2	94.4	97.2	91.7
YOLOv10s	87.7	98.2	94.4	98.0	94.6
YOLO11s	94.4	98.5	95.0	97.0	96.2
Ours	94.7	99.5	95.5	97.6	96.8