Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.14587 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910004651491328 |
|---|---|
| author | Kozák, Viktor Košnar, Karel Chudoba, Jan Kulich, Miroslav Přeučil, Libor |
| author_facet | Kozák, Viktor Košnar, Karel Chudoba, Jan Kulich, Miroslav Přeučil, Libor |
| contents | Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and viewing angles. This paper presents a novel localization pipeline that directly integrates PV module detection with UAV navigation, allowing precise positioning during inspection. The detections are used to identify the power plant structures in the image. These are associated with the power plant model and used to infer the UAV position relative to the inspected PV installation. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. Additionally, we present three different methods for visual segmentation of PV modules and evaluate their performance in relation to the proposed localization pipeline. The presented methods were verified and evaluated using custom aerial inspection data sets, demonstrating their robustness and applicability for real-time navigation. Additionally, we evaluate the influence of the power plant model precision on the localization methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection Kozák, Viktor Košnar, Karel Chudoba, Jan Kulich, Miroslav Přeučil, Libor Computer Vision and Pattern Recognition Robotics Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and viewing angles. This paper presents a novel localization pipeline that directly integrates PV module detection with UAV navigation, allowing precise positioning during inspection. The detections are used to identify the power plant structures in the image. These are associated with the power plant model and used to infer the UAV position relative to the inspected PV installation. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. Additionally, we present three different methods for visual segmentation of PV modules and evaluate their performance in relation to the proposed localization pipeline. The presented methods were verified and evaluated using custom aerial inspection data sets, demonstrating their robustness and applicability for real-time navigation. Additionally, we evaluate the influence of the power plant model precision on the localization methods. |
| title | Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2501.14587 |