# Automated Damage Inspection of Power Transmission Towers from UAV Images

arXiv: https://arxiv.org/abs/2111.15581 · HTML: https://mlsanity.com/paper/2111.15581/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 16 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [CenterMask : Real-Time Anchor-Free Instance Segmentation](https://mlsanity.com/paper/1911.06667/) (2019)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [HarDNet: A Low Memory Traffic Network](https://mlsanity.com/paper/1909.00948/) (2019)

## Built on by (most cited first, 1 of 1)

- [Automatic Drywall Analysis for Progress Tracking and Quality Control in Construction](https://mlsanity.com/paper/2503.03422/) (2025)

## Abstract (arXiv)

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.