Automatic Small Defect Detection in Unmanned Aerial Vehicle Images of Power Transmission Lines using DRSPTL

Document Type : Original Article

Authors

1 Shahid Bahonar University-Kerman-Iran

2 Faculty of Physics, Shahid Bahonar University, Kerman, Kerman, Iran

Abstract

Recently, small object recognition based on deep learning techniques has gained particular attention in many practical applications and is challenging because small objects have low resolution and do not contain detailed information.  In this article, a new two-stage detector based on detecting objects with recursive feature pyramid and switchable atrous convolution (DetectoRS) has been introduced to find small and important defects such as loose nut-bolts and missing-nut in power transmission lines (PTL). The architecture of DetectoRS was necessarily modified. The proposed technique which is called DRSPTL, the Cascade R-CNN with ResNext-101 is used to increase the accuracy of small defect detection. In this work, high-resolution RGB images are captured by unmanned aerial vehicles (UAVs) imaging PTL from Tehran, Kerman, Shiraz, Isfahan, and Ahwaz regional electric companies, Iran. The training and test datasets from the captured faulty images are created from annotation by experts. To construct the training dataset, nearly eighty percent of the whole set of faulty images were selected and labeled. The performance of the proposed method with two state-of-the-art object detection techniques RetinaNet and RepPoints has been compared. DRSPTL has the highest small defect detection accuracy. It is noteworthy that the obtained results could significantly reduce the time and cost of electric power companies by detecting the defects automatically and preventing the occurrence of many power outages.

Keywords

References

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Advanced Signal Processing
Volume 4, Issue 2 - Serial Number 6
December 2020
Pages 159-170

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