Drone inspection dataset. It consists of 701 drone inspection images of WT.
Drone inspection dataset Feb 20, 2019 · Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. Dataset containing IR, visible and audio data that can be used to train and evaluate drone detection sensors and systems. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in Jan 24, 2019 · It is to our knowledge, the only public wind turbine drone inspection image dataset containing a total of. This dataset has temporal inspection images of 2017 and 2018 Blade30 is a comprehensive dataset for multiple blade-related tasks, including blade stitching, segmentation, defect detection, classification and deduplication, contamination detection and classification, and more. 1 Dataset 3. drone imagery) and annotations of solar panel locations captured from controlled flights at various altitudes and speeds across two sites at Duke Forest (Couch field and Blackwood field). Together they form a unique fingerprint. Sep 25, 2018 · This dataset set has temporal inspection images for the years of 2017 and 2018 of the same 'Nordtank' wind turbine at DTU wind facilities in Roskilde, Denmark. 5cm, etc. Especially, if you are interested in detecting anomalies (here broken glass insulators) present on electrical towers by computer vision methods. To evaluate the performance of the fine-tuned classification models, we used AUC-ROC metric which is the area under the receiver operating characteristic curve. This dataset contains unmanned aerial vehicle (UAV) imagery (a. We used the COn- Jan 1, 2022 · To encourage the development of automated visual inspection and damage detection solutions in the realm of infrastructure management, we propose BiNet, a visual inspection dataset for multi-label . It consists of 701 drone inspection images of WT. Images in the UAVDT dataset have bounding box annotations. This dataset set has temporal inspection images for the years of 2017 and 2018 of the same 'Nordtank' wind turbine at DTU wind facilities in Roskilde, Denmark. See full list on github. Most studies on drone detection fail to specify the type of acquisition device, the drone type, the Sep 10, 2018 · This dataset set has temporal inspection images for the years of 2017 and 2018 of the same 'Nordtank' wind turbine at DTU wind facilities in Roskilde, Denmark. Automating power line visual inspections remains a relevant open problem for the industry due to the lack of public real-world datasets of power line Feb 10, 2023 · Power line inspection is an important part of the smart grid. It is used in the drone inspection domain. Sep 26, 2019 · Drone Dataset (UAV) is a dataset for an object detection task. 1 Image collection. com The dataset for drone based detection and tracking is released, including both image/video, and annotations. In total there are 423 stationary images and corresponding annotations of solar panels within sight, along with 60 videos taken from flying Power line maintenance and inspection are essential to avoid power supply interruptions, reducing its high social and financial impacts yearly. Audio labels: Drone, Helicopter and Background. Nov 2, 2021 · The use of small and remotely controlled unmanned aerial vehicles (UAVs), or drones, has increased in recent years. Blade30 is a comprehensive dataset for multiple blade-related tasks, including blade stitching, segmentation, defect detection, classification and deduplication, contamination detection and classification, and more. Drones offer a faster, safer, and more cost-effective inspection to collect images from wind turbine blades at different times. Drones are a great tool for bridge inspectors because they bring flexibility and access to the inspection. Sep 14, 2021 · A dataset of wind turbine surface damage composed of images from Shihavuddin & Chen's (2018) dataset split into 586x371 pixel images with YOLO format annotations for Dirt and Damage. 1. 17632/hd96prn3nc. From the pool of available images, 60% were used for training and Oct 17, 2023 · In the context of difficulty in detection problems and the limited computing resources of various fault scales in aerial images of transmission line UAV inspections, this paper proposes a TD-YOLO algorithm (YOLO for transmission detection). (2018). We evaluate the transfer learning approach by first training the VGG16 on a domain-specific dataset. This dataset contains temporal inspection images of 2017 and 2018. The surface damage suggestion system is trained using faster R-CNN [20], which is a dataset” owned by EasyInspect ApS company, comprising drone inspection of different types of wind turbine blades located in Denmark. The dataset used for this work is DTU images of the WTB []. MPID is built by merging diverse datasets of insulator images taken during drone inspections of transmission lines (see table 1). , and discover GSD Samples: 0. 2 Feb 20, 2019 · It is the only public wind turbine drone inspection image dataset containing a total. , BiNet). Nov 2, 2023 · Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. , ImageNet) to the target dataset (e. of 701 high-resolution images. , & Chen, X. We form a set of insulator images from different times of the day, different seasons, different weather, different lighting, different angles, different cameras, and different regions of the world. Images in the Drone Dataset (UAV) dataset have bounding box annotations. Jan 10, 2023 · 3. Categories Wind Energy , Wind Turbine , Building Inspection , Drone (Aircraft) You will find in this repo a dataset [ images + annotations (Yolov7 format)] which I hope will be useful for your work (view samples from the dataset on figure 1). UAVDT Dataset is a dataset for an object detection task. g. The dataset is hosted within the Mendeley public dataset repository [19]. Possible applications of the dataset could be in the drone inspection domain. However, in the power line inspection based on computer vision, datasets have a significant impact Dec 12, 2021 · By applying drones in visual inspections the amount of images is increasing even further. Firstly, the Ghost module is used to lighten the model’s feature extraction network and prediction network, significantly reducing the number of BiNet: Bridge Visual Inspection Dataset and Approach 1031 Pretrained VGG16 with CODEBRIM Transfer learning aims to transfer the learned representations from the source dataset (e. High-resolution geospatial drone data sets from RGB, Thermal, LiDAR sensors, etc. Publication of the wind turbine inspection dataset: This work produced a publicly-available drone inspection image of the “Nordtank” turbine over the years of 2017 and 2018. COCO-Bridge: Dataset and Benchmark for Structural Detail Detection Eric Bianchi GENERAL AUDIENCE ABSTRACT Common Objects in Context for bridge inspection (COCO-Bridge) was introduced to improve a drone-conducted bridge inspection process. Automated analysis of these inspection images with To encourage the development of automated visual inspection and damage detection solutions in the realm of infrastructure management, we propose BiNet, a visual inspection dataset for multi-label damage identification that can be used for classification, localisation, and object detection. k. Explore a wide array of Drone Data Sets / UAV Data Sets free of charge. a. Video labels: Airplane, Bird, Drone and Helicopter. SHIHAVUDDIN, ASM; Chen, Xiao (2018), “DTU - Drone inspection images of wind turbine”, Mendeley Data, V2, doi: 10. Shihavuddin, ASM. The dataset consists of 1359 images with 1486 labeled objects belonging to 1 single class (drone). This goes in parallel with misuse episodes, with an evident threat to the safety of people or facilities. As a result, the detection of UAV has also emerged as a research topic. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. 8cm, 1cm, 2. 701 high-resolution images. Dive into the research topics of 'DTU - Drone inspection images of wind turbine'. This dataset comes with the damages such as Vortex Generator (VG) panel with missing teeth, VG panel, Erosion of Leading Edge (LE), cracks, damaged lightning receptor and others. The dataset consists of 77819 images with 835879 labeled objects belonging to 4 different classes including car, vehicle, truck, and other: bus. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. Therefore a drone-enabled inspection coupled with vision-based technology has the potential to serve as a more economical and safe alternative to conventional inspection practices . Blade30 was collected during the real drone-based wind turbine inspection in various environments. Download scientific diagram | Pictures from DTU‐Drone inspection images of wind turbine dataset from publication: Portable motorized telescope system for wind turbine blades damage detection AI-AR for Bridge Inspection by Drone 307 We split the dataset CODEBRIM into training, validation, and test set with a ratio of 70%, 20%, and 10% respectively. In order to improve and automatise further visual inspections, we aim to Nov 1, 2024 · Traditional manual inspections of blades involve time-consuming, dangerous, and expensive techniques such as lifts and rope access and require the wind turbine to be taken out of service. irdafiyvxiywammktcdvljvvpkrewqxbgbavqvefhxmqyhinh