Vision-Based Power Line Cables and Pylons Detection for Low Flying Aircraft
This addresses a safety-critical issue for pilots in low-visibility conditions, but it is incremental as it builds on existing deep learning techniques.
The paper tackles the problem of detecting power line cables and pylons from aircraft-mounted cameras to enhance safety for low-flying aircraft, achieving performance that outperforms prior methods on two benchmarking datasets.
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.