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5 results for “Drone Count”
Drone videos and their annotations of passing sheep (for counting purpose)
<p>The data below are part of the European H2020 project ICAERUS regarding the livestock monitoring use case. More information here : https://icaerus.eu/</p> <p>Counting sheep is particularly challenging for farmers with hundreds of animals in a flock. Our objective is to develop methodology based on computer vision to count sheep when they are passing in a corridor to coming back in a park or a pen. This first dataset will support our work. </p> <p>The dataset encompasses 4 .MP4 videos from drone (DJI mavic 3 Enterprise or Thermal) of around 50 sheep crossing a gate. <br>The videos were taken from 5m to 10m of height and to an horizontal distance of the gate from 0m to 10m. </p> <p>The videos come from previous datasets (https://doi.org/10.5281/zenodo.10400302) but have been modified to facilitate annotation: the duration of each video has been reduced to 30 seconds or less with 9 frames per second. Image size has been modified to 1440x1080 pixels.<br>Annotation files are available in MOTS and YOLO formats.</p> <p>This dataset encompasses the following data:<br>-----Videos: a directory were the videos are stored (4 videos, RGB images taken from 5 or 10m of altitude; images size of 1440x1080, videos taken with DJI MAVIC3T).<br>-------------crop_23.11.23-XX: a directory by flight containing the video of a unique flight, with the date (YY.MM.DD) and XX representing a mission number<br>-----MOT1.1: a directory with the annotations of cows at the MOTS format<br>-------------MOT1.1_crop_23.11.23-XX: a directory by flight containing the annotations, one annotation file referred to an unique image and have the same name except the extension<br>-----YOLO1.1: a directory with the annotations of cows at the YOLO format<br>-------------YOLO1.1_crop_23.11.23-XX: a directory by flight containing the annotations, one annotation file referred to an unique image and have the same name except the extension</p> <p>More videos will be published in the next months.</p> <p>For more information, please contact: adrien.lebreton@idele.fr </p> <p>The authors are opened to any collaborations on this topic.</p>
Data from: Drones count wildlife more accurately and precisely than humans
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Año Nuevo Island Animal Count: analyzing citizen science pinniped counts from drone imagery
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Drones, automatic counting tools and artificial neural networks in wildlife population censusing
<p>1. The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non-breeding periods was investigated.</p> <p>2. In 96% of 343 cases, drone counting was successful. 18.8% of non-breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone.</p> <p>3. The automatic counting of birds was best done with ImageJ/Fiji microbiology software – the average counting rate was 100 birds in 64 seconds.</p> <p>4. Machine learning using neural network algorithms proved to be an effective and quick way of counting birds – 100 birds in 7 seconds. However, the preparation of images and machine learning time is time-consuming, so this method is recommended only for large data sets and large bird assemblages.</p> <p>5. The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behaviour of the target animals.</p>
Drones, automatic counting tools and artificial neural networks in wildlife population censusing
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