Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft

TL;DR

This paper presents a deep learning-based vision system for detecting power lines and pylons from aircraft images, enhancing low-altitude flight safety in low-visibility conditions.

Contribution

It introduces a unified convolutional neural network with transfer learning and a specialized loss function for detecting curvilinear structures like power lines and pylons from long-distance images.

Findings

Outperforms previous distant cable detection methods

Successfully detects pylons with annotated data

Demonstrates real-time onboard flight performance

Abstract

Power lines are dangerous for low-flying aircraft, especially in low-visibility conditions. Thus, a vision-based system able to analyze the aircraft's surroundings and to provide the pilots with a "second pair of eyes" can contribute to enhancing their safety. To this end, we have developed a deep learning approach to jointly detect power line cables and pylons from images captured at distances of several hundred meters by aircraft-mounted cameras. In doing so, we have combined a modern convolutional architecture with transfer learning and a loss function adapted to curvilinear structure delineation. We use a single network for both detection tasks and demonstrated its performance on two benchmarking datasets. We have integrated it within an onboard system and run it in flight, and have demonstrated with our experiments that it outperforms the prior distant cable detection method on both datasets, while also successfully detecting pylons, given their annotations are available for the data.