Automated Damage Inspection of Power Transmission Towers from UAV Images

TL;DR

This paper presents an automated system for detecting structural damage in power transmission towers using UAV images, addressing data scarcity, ambiguity, and class imbalance challenges to facilitate remote infrastructure inspection.

Contribution

It introduces a damage detection system tailored for drone images of transmission towers, employing techniques to handle limited and ambiguous training data.

Findings

Effective damage detection on UAV images demonstrated

Addressed data scarcity and ambiguity challenges

Showed viability of automated inspection approach

Abstract

Infrastructure inspection is a very costly task, requiring technicians to access remote or hard-to-reach places. This is the case for power transmission towers, which are sparsely located and require trained workers to climb them to search for damages. Recently, the use of drones or helicopters for remote recording is increasing in the industry, sparing the technicians this perilous task. This, however, leaves the problem of analyzing big amounts of images, which has great potential for automation. This is a challenging task for several reasons. First, the lack of freely available training data and the difficulty to collect it complicate this problem. Additionally, the boundaries of what constitutes a damage are fuzzy, introducing a degree of subjectivity in the labelling of the data. The unbalanced class distribution in the images also plays a role in increasing the difficulty of the task. This paper tackles the problem of structural damage detection in transmission towers, addressing these issues. Our main contributions are the development of a system for damage detection on remotely acquired drone images, applying techniques to overcome the issue of data scarcity and ambiguity, as well as the evaluation of the viability of such an approach to solve this particular problem.