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A Deep Learning Model for Small-size Defective Components Detection in Power Transmission Tower.

Authors :
Jiao, Runhai
Liu, Yanzhi
He, Hui
Ma, Xuehai
Li, Zuyi
Source :
IEEE Transactions on Power Delivery. Aug2022, Vol. 37 Issue 4, p2551-2561. 11p.
Publication Year :
2022

Abstract

Unmanned Aerial Vehicle (UAV) inspection has gradually replaced manual inspection of transmission tower, which produces many images. While it is laborious and time-consuming to manually analyze these images, there are also challenges in automatically detecting small-size defective components such as bolts in transmission tower images, due to problems including complex background, small size, and many similar objects of bolts. In this paper, by virtue of multi-scale features and context information, we propose a deep neural network named Camp-Net (Context Information and Multi-Scale Pyramid Network) to identify bolts defect in transmission tower images. First, multi-scale feature fusion combines deep features and shallow features in convolutional networks to detect small-size bolts. Second, context information fusion puts the information around bolts into the detection network to remove the disturbance of complex background and similar objects. An image dataset containing defective bolts and normal bolts is constructed for model training and testing. Experimental results show that bolts with loose pins and bolts without pins among fittings in transmission tower can be accurately identified with the proposed model. The Average Precision (AP) of defective bolts detection of this model can be 11.4% higher than that of the commonly used high performance model, Faster R-CNN. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08858977
Volume :
37
Issue :
4
Database :
Academic Search Index
Journal :
IEEE Transactions on Power Delivery
Publication Type :
Academic Journal
Accession number :
158186356
Full Text :
https://doi.org/10.1109/TPWRD.2021.3112285