2024/07/19 by Jakub Gwizdała, Doruk Oner, Gwizdała, Jakub +15 · 1 voice
Computer Science · Engineering · #Advanced Measurement and Detection Methods #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Power Line Inspection Robots #Vehicle License Plate Recognition #cs.CV
paper · pdf · doi:10.48550/arxiv.2407.14352
openalex publication_date 2024/07/19 · arxiv published 2024/07/19 · arxiv updated 2024/07/30 · openalex created_date 2024/08/01 · openalex updated_date 2026/07/28
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.