基于SE-YOLOv5s的绝缘子检测

doi:10.11959/j.issn.2096-6652.202132

Abstract

Abstract:

In a large environment where the power system needs to be inspected, the traditional method of manual inspection has great inconvenience and potential safety risks, and the object detection method of unmanned aerial vehicle has great application prospects in the direction of insulator detection and recognition.SE-YOLOv5s, a lightweight insulator detection network that performs efficient detection for this task was presented.Firstly, backbone of YOLOv5s by fusing the SE attention module was strengthen.Then, the position distribution of insulator object was investigated and predefined templates of a priori box by K-means clustering on prior coordinate vectors were generated.Finally, the network by a multitask loss function was trained combined with confidence and position regression task.Furthermore, Mosaic data augmentation was utilized to supplement additional training samples.Experimental results demonstrate that the proposed SE-YOLOv5s significantly outperforms baseline methods at multiple criterions including accuracy, recall rate, detection rate and mean average precision.In comparison with the baselines, the proposed network has a flexible trade-off between robustness and memory overhead and it is a potential approach to promote the power system development.

Key words: insulator, object detection, deep learning, YOLOv5s

CLC Number:

Qing TIAN, Rong HU, Zuoyong LI, et al. Insulator detection based on SE-YOLOv5s[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 312-321.

Figures/Tables 12

References 19

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α	准确率	召回率
-	98.24%	99.46%
0.6	98.16%	99.17%
0.7	98.19%	99.28%
0.8	98.19%	99.32%
0.9	98.21%	99.43%

模型	mAP	平均检测时间/ms
Faster R-CNN	95.58%	163.7
SSD	95.22%	53.5
YOLOv3^[19]	97.02%	64.4
YOLOv5	98.24%	36.2
SE-YOLOv5s	98.46%	19.8

Insulator detection based on SE-YOLOv5s

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