Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection

• URL: http://arxiv.org/abs/2501.14587v1
• Date: Fri, 24 Jan 2025 15:48:41 GMT
• Title: Visual Localization via Semantic Structures in Autonomous Photovoltaic Power Plant Inspection
• Authors: Viktor Kozák, Karel Košnar, Jan Chudoba, Miroslav Kulich, Libor Přeučil,
• Abstract summary: This paper presents a novel localization pipeline that integrates PV module detection with UAV navigation. Detections are used to identify the power plant structures in the image and associate these with the power plant model. We present three distinct methods for visual segmentation of PV modules based on traditional computer vision, deep learning, and their fusion.
• Score: 0.6291443816903801
• License:
• Abstract: 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. Detections are used to identify the power plant structures in the image and associate these with the power plant model. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. We present three distinct methods for visual segmentation of PV modules based on traditional computer vision, deep learning, and their fusion, and we 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's precision on the localization methods.

Related papers

• Machine learning approaches for automatic defect detection in photovoltaic systems [1.121744174061766]
  Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation. Continuous monitoring of PV modules during operation via unmanned aerial vehicles is essential. Computer vision provides an automatic, non-destructive and cost-effective tool for monitoring defects in large-scale PV plants.
  arXiv  Detail & Related papers  (2024-09-24T13:11:05Z)
• UAV-Based Human Body Detector Selection and Fusion for Geolocated Saliency Map Generation [0.2499907423888049]
  The problem of reliably detecting and geolocating objects of different classes in soft real-time is essential in many application areas, such as Search and Rescue performed using Unmanned Aerial Vehicles (UAVs) This research addresses the complementary problems of system contextual vision-based detector selection, allocation, and execution. The detection results are fused using a method for building maps of salient locations which takes advantage of a novel sensor model for vision-based detections for both positive and negative observations.
  arXiv  Detail & Related papers  (2024-08-29T13:00:37Z)
• Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems? [0.8192907805418583]
  Remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. We leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions.
  arXiv  Detail & Related papers  (2023-09-21T16:15:56Z)
• Energy-Based Models for Cross-Modal Localization using Convolutional Transformers [52.27061799824835]
  We present a novel framework for localizing a ground vehicle mounted with a range sensor against satellite imagery in the absence of GPS. We propose a method using convolutional transformers that performs accurate metric-level localization in a cross-modal manner. We train our model end-to-end and demonstrate our approach achieving higher accuracy than the state-of-the-art on KITTI, Pandaset, and a custom dataset.
  arXiv  Detail & Related papers  (2023-06-06T21:27:08Z)
• Real-time Aerial Detection and Reasoning on Embedded-UAVs [3.0839245814393728]
  We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition.
  arXiv  Detail & Related papers  (2023-05-21T09:43:17Z)
• MOST: Multiple Object localization with Self-supervised Transformers for object discovery [97.47075050779085]
  We present Multiple Object localization with Self-supervised Transformers (MOST) MOST uses features of transformers trained using self-supervised learning to localize multiple objects in real world images. We show MOST can be used for self-supervised pre-training of object detectors, and yields consistent improvements on fully, semi-supervised object detection and unsupervised region proposal generation.
  arXiv  Detail & Related papers  (2023-04-11T17:57:27Z)
• Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing [77.34726150561087]
  This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. All algorithms have been developed using state-of-the-art image processing methods based on deep neural networks.
  arXiv  Detail & Related papers  (2022-11-02T11:10:42Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• Continuous Self-Localization on Aerial Images Using Visual and Lidar Sensors [25.87104194833264]
  We propose a novel method for geo-tracking in outdoor environments by registering a vehicle's sensor information with aerial imagery of an unseen target region. We train a model in a metric learning setting to extract visual features from ground and aerial images. Our method is the first to utilize on-board cameras in an end-to-end differentiable model for metric self-localization on unseen orthophotos.
  arXiv  Detail & Related papers  (2022-03-07T12:25:44Z)
• Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
  We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process. The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
  arXiv  Detail & Related papers  (2021-04-22T12:47:37Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.