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8 results for “Litter detection”
PlastOPol: A Dataset for Litter Detection
<p>PlastOPol dataset aiming of giving to the computer science and environmental science communities a new set of images with the presence of litter in several types of environments. We hope that PlastOPol serves as a basis for the proposal of automatic detection methods which can support the furthering Sensors 2022, 1, 0 6 of 20 of research on litter in the environment. The images were collected by the Marine Debris Tracker available under an open access Creative Commons Attribution license. Building the dataset involved the meticulous task of labeling each litter instance in each image. PlastOPol is a one-class labeled dataset, where all the data corresponds to the “litter” class as its super-category. This dataset has 2418 images collected by the Marine Debris Tracker with a total of 5300 instances of litter. Each instance is wrapped within a rectangular bounding box represented by four values (x1, y1, width, and height), where (x1, y1) corresponds to the upper left corner of the bounding box.</p> <p>If you use this dataset, please cite our paper:</p> <p>@Article{Cordova2022Sensors,<br> AUTHOR = {Córdova, Manuel and Pinto, Allan and Hellevik, Christina Carrozzo and Alaliyat, Saleh Abdel-Afou and Hameed, Ibrahim A. and Pedrini, Helio and Torres, Ricardo da S.},<br> TITLE = {Litter Detection with Deep Learning: A Comparative Study},<br> JOURNAL = {Sensors},<br> VOLUME = {22},<br> YEAR = {2022},<br> NUMBER = {2},<br> ARTICLE-NUMBER = {548},<br> URL = {https://www.mdpi.com/1424-8220/22/2/548},<br> ISSN = {1424-8220},<br> DOI = {10.3390/s22020548}<br> }</p> <p> </p>
TUD-GV Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia T, Vallendar AJ, de Vries R, Kapelan Z and Taormina R (2023) Advancing deep learning-based detection of floating litter using a novel open dataset. <em>Front. Water</em> 5:1298465. doi: 10.3389/frwa.2023.1298465.</p> <p>The large-scale "TU Delft - Green Village" (TUD-GV) dataset is for detecting floating litter with computer vision. We created this dataset from experiments conducted during 10 days in February and April 2021 in a small drainage canal at The Green Village — a field lab facility in the TU Delft Campus, the Netherlands. We captured data using two action cameras (GoPro HERO4 and GoPro MAX 360) and a phone (Huawei P30 Pro) mounted on four different locations on a bridge. All devices recorded videos with a resolution of 1080p, a linear field of view, and a FPS (frame per second) of 24 (for the action cameras) or 30 (for the phone).</p> <p>This dataset consists of 9,473 RGB images. We manually labeled the images in the TUD-GV dataset into four classes: <em>no litter</em> (0 items), <em>little litter</em> (1-2 items), <em>moderate litter</em> (3-5 items), and <em>lots of litter</em> (6-10 items) according to the number of litter items in images.</p> <p>This dataset is stored in the ZIP file contain 77 directories and the TUD-GV.xls file. Each of these directories contain four class label directories, and each label directory contains JPG images. The TUD-GV.xls file contains the detailed information of images in 77 directories, including collecting date, collecting time, device, device location (in a bridge), device degree, device height, weather conditions, litter source, the number of images per class, and the number of images in total.</p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <p>@article{jia2023advancing, title={Advancing Deep Learning-based Detection of Floating Litter using a Novel Open Dataset}, author={Jia, Tianlong and Vallendar, Andre Jehan and de Vries, Rinze and Kapelan, Zoran and Taormina, Riccardo}, journal={Frontiers in Water}, volume={5}, pages={1298465}, publisher={Frontiers}, year={2023} }</p>
pLitterStreet - Street Level Plastic Litter Detection Dataset
<p><strong>pLitterStreet</strong> dataset comprises of more than <em>13,000 images</em>. These images were captured using <em>vehicle-mounted cameras</em> that were strategically positioned to focus on the sides of streets. The primary objective of this dataset is to facilitate research related to street litter and its impact on the environment.</p> <p>Annotations for the images are provided in the widely-used Microsoft COCO JSON format. Image in the dataset is fully annotated, enabling the identification and categorization of various types of litter found along urban and rural streets. These annotations include precise labeling of litter items, making the dataset an invaluable resource for developing and evaluating object detection and image recognition models.</p>
Plastic Litter Project 2022-2023 - Minimum Detection Fraction Dataset
<p>Sentinel-2 and PlanetScope SuperDove data, with corresponding UAS RGB reference images (where available) used in </p>
TUD-GV Dataset for Floating Litter Detection (object detection task)
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>This dataset is a subset of the large-scale "TU Delft - Green Village" (TUD-GV), which includes 9,473 RGB images. More details on the TUD-GV dataset can be found at: <a href="https://doi.org/10.5281/zenodo.7636124" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7636124</a>. This subset used in this publication consists of 1,501 images, selected from the full TUD-GV dataset. All floating litter items in this subset have been annotated with bounding boxes. This subset is specifically for detecting floating litter in object detection tasks.</p> <p>The 1,501 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file.</p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Amsterdam Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>The Amsterdam dataset is for detecting floating litter with computer vision. We created the Amsterdam dataset from one experiment conducted on 1st March 2023, in canals and ponds at Amsterdam, the Netherlands. We recorded images using an action camera (GoPro Hero 10). All images used in this publication are captured by the device positioned at a distance of maximum 2 m from the water surface. The image resolution is 5568*4176. This dataset consists of 9 RGB images. We manually labeled the litter items in these images with bounding boxes.</p> <p>The 9 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file.</p> <p> </p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Oostpoort Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>The Oostpoort dataset is for detecting floating litter with computer vision. We generated this dataset from experiments conducted during 26 days from February to March 2022, in a canal at Oostpoort, Delft, the Netherlands. We collected data employing action cameras (GoPro MAX 360 and GoCam3) mounted outside the windows of a tower at Oostpoort with a viewing angle of 0 degree. We recorded video sequences with a time-lapse recording (1 image/30 sec) and a FPS (frame per second) of 17.98. We generated the Oostpoort dataset by saving images from these videos. The resolution of images are 3840*2160 and 1920*1440. This dataset consists of 562 RGB images. We manually labeled the litter items in these images with bounding boxes.</p> <p>The 562 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file. The <em>Oostpoort Dataset.xlsx</em> file contains the detailed information of images, including collecting date, collecting time, device, device location (in a bridge), device degree, device height, weather conditions, the number of images, and the number of annotated litter items.</p> <p> </p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
Groningen Dataset for Floating Litter Detection
<p>This dataset contains the data used for the publication:</p> <p>Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. <em>Water Research</em>, <em>266</em>, 122405.</p> <p>The Groningen dataset is for detecting floating litter with computer vision. We conducted several experiments in a canal in Groningen, the Netherlands, in 2023. We captured data employing a security cameras (Obscape HQ time-lapse), mounted on a bridge at a height of 4m. We recorded images with a time-lapse recording (1 image/6 sec). The image resolution is 2592*1944. This dataset consists of 63 RGB images. We manually labeled the litter items in these images with bounding boxes.</p> <p>The 63 images are stored in the <em>images.zip</em> file, the annotations are stored in the <em>labels_txt.zip</em> file, and the class of the annotation (i.e., litter) is stored in the <em>classes.txt</em> file. </p> <p> </p> <p>If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:</p> <pre>@article{jia2024detecting, title={Detecting floating litter in freshwater bodies with semi-supervised deep learning}, author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo}, journal={Water Research}, volume={266}, pages={122405}, year={2024}, publisher={Elsevier} }</pre>
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