Real-Time Detection of Insulator Drop String Based on UAV Aerial Photography

doi:10.16183/j.cnki.jsjtu.2021.416

Abstract

Abstract:

It is of great significance for unmanned aerial vehicle(UAV) to replace manual inspection of power insulators. Aimed at the problem of limited computing power and storage resources of the UAV, an improved real-time target detection algorithm suitable for insulator drop string failure detection is proposed. Based on the YOLOv5s detection network, first, the PANet networks in neck are replaced with bi-directional feature pyramid network(BiFPN) to improve the feature fusion ability. Next, DIoU is used to optimize the loss function to optimize the model. The channel pruning and fine tuning of the γ coefficient generally improve the accuracy, speed, and deployment ability of the detection network. Finally, the image is enhanced at the network output to improve the availability of the algorithm. The proposed algorithm is tested under a specially expanded insulator fault data set. The results show that compared with the original YOLOv5s algorithm, the average accuracy of the proposed algorithm is improved by 3.91%, the detection speed is improved by 25.6%, and the model volume is reduced by 59.1%.

Key words: unmanned aerial vehicle (UAV), insulator drop string, bi-directional feature pyramid network (BiFPN), γ coefficient pruning fine adjustment, DIoU loss function, image enhancement

CLC Number:

TM855

LI Dengpan, REN Xiaoming, YAN Nannan. Real-Time Detection of Insulator Drop String Based on UAV Aerial Photography[J]. Journal of Shanghai Jiao Tong University, 2022, 56(8): 994-1003.

Figures/Tables 20

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Tab.1

Summary of test results of all network models

组别	网络模型	mAP(50%)			$t -$ /ms	N_RAM/MB	体积/MB
组别	网络模型	绝缘子整体	掉串故障位置	平均值	$t -$ /ms	N_RAM/MB	体积/MB
1	YOLOv5s	0.908	0.798	0.853	12.4	7.02	13.7
2	YOLOv5s+数据集扩充	0.927	0.861	0.894	12.5	7.02	13.7
3	YOLOv5s+数据集扩充+DIoU	0.939	0.862	0.901	12.5	7.02	13.7
4	YOLOv5s+数据集扩充+BiFPN	0.929	0.847	0.888	12.2	7.08	13.9
5	YOLOv5s+数据集扩充+ BiFPN +DIoU	0.945	0.868	0.907	12.7	7.08	13.9
6	YOLOv5s+数据集扩充+ BiFPN + DIoU+γ系数剪枝微调	0.951	0.906	0.929	9.3	2.79	5.60

Tab.1

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Tab.2

Summary of test data of lightweight networks

网络模型	mAP(50%)			$t -$ /ms	N_RAM/ MB	体积/ MB
网络模型	绝缘子整体	掉串故障位置	平均值	$t -$ /ms	N_RAM/ MB	体积/ MB
YOLOv3-tiny	0.901	0.897	0.899	5.4	8.67	16.6
YOLOv5n	0.883	0.636	0.760	9.5	1.76	3.74
YOLOv5s	0.927	0.861	0.894	12.5	7.02	13.7
YOLOXs	0.977	0.909	0.943	12.7	8.94	68.5
本文	0.951	0.906	0.929	9.3	2.79	5.60

Tab.2

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