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Dataset results
118 results for “Object detection”
Apple object detection code
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Apple Object Detection Dataset
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Yellow Sticky Trap Insect Images for Object Detection Training
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raycast: multi-view object detection in UAV image clouds (case-study data)
<p>This package contains image data for conducting multi- and single-view sewer inlet detection in UAV image clouds. The package consists in: - individual UAV images, taken with high overlap and corrected for lens distortion - orthophoto of the case study area, clipped to road boundaries.</p>
High proper motion objects detected in unTimely W2 band data
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IV-YOLO: A Lightweight Dual-Branch Object Detection Network
<p>IV-YOLO 的网络和数据存储</p>
TRIBE AGROset - An agricola visual dataset for object detection
<p>This a computer vision dataset for object detection of fruits and trunks in agriculture. In its current stage, the dataset has 22 classes, such as:</p> <ul> <li>background</li> <li>arugula_minus9 (arugula in an early stage of development with less than 9 leaves)</li> <li>arugula_plus9 (arugula in an advanced stage of development with more than 9 leaves)</li> <li>carrot</li> <li>carrot_small_leaves</li> <li>coriander</li> <li>coriander_small_leaves</li> <li>coty (lettuce in the starting stage of development, with about two very small leaves)</li> <li>grape_bunch (bunches of full-size grapes)</li> <li>grape_bunch_small (bunches of pee-size grapes)</li> <li>lettuce_minus9 (lettuce in an early stage of development with less than 9 leaves)</li> <li>lettuce_plus9 (lettuce in an advanced stage of development with more than 9 leaves)</li> <li>lettuce_ready (lettuce ready for being harvested)</li> <li>radish_root</li> <li>radish_small_leaves</li> <li>radish_big_leaves</li> <li>spinach</li> <li>spinach_big</li> <li>swiss_chard</li> <li>tomato</li> <li>trunk (vine and forestry trunk)</li> <li>turnip</li> <li>turnip_small_leaves</li> </ul> <p>All the images were self-acquired in other datasets, under real-world conditions and using typical visual sensors for agricultural robots and IoT. This dataset is the result of the merging of some other datasets after fixing some annotations.</p> <p>Magalhães, Sandro A. ‘Dataset of Tomato inside Greenhouses for Object Detection in Pascal VOC’. INESC TEC, 2021. https://doi.org/10.25747/pc1e-nk92.</p> <p>Aguiar, André Silva, and Sandro Magalhães. ‘Grape Bunch and Vine Trunk Dataset for Deep Learning Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5139598.</p> <p>Rodrigues, Leandro, Francisco Terra, Sandro Magalhães, Filipe Santos, Pedro Moura, and Mário Cunha. ‘PixelCropRobot Dataset: Images of Vegetables Crops in Different Phenological Stages Taken in Greenhouses’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.7433286.</p> <p>Moreira, Germano, Sandro Augusto Magalhães, Tiago Padilha, Filipe Neves dos Santos, and Mário Cunha. ‘RpiTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596363.</p> <p>Sarmento, José, Filipe Neves Dos Santos, and André Silva Aguiar. ‘VinePoint’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5038646.</p> <p>Magalhães, Sandro Augusto, Germano Moreira, Filipe Neves dos Santos, and Mário Cunha. ‘AgRobTomato Dataset: Greenhouse Tomatoes with Different Ripeness Stages’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5596799.</p> <p>Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDet -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection’. Zenodo, 2021. https://doi.org/10.5281/ZENODO.5213825.</p> <p>Da Silva, Daniel Queirós, and Filipe Neves Dos Santos. ‘ForTrunkDetV2 -- Forest Dataset of Visible and Thermal Annotated Images for Object Detection (Augmented Version)’. Zenodo, 2022. https://doi.org/10.5281/ZENODO.7186052.</p> <p> </p>
Datasets and models for Object detection
<p>This repository includes the datasets and models for object detection. </p> <p>The "Datasets" folder includes the training and testing datasets for object detection.( Both Tf-record and Images are provided)</p> <p>The models includes a pre-trained model from TensorFlow 2 model zoo and our trained model. The "Custom_model" folder includes the training log and check points.</p>
Action recognition and object detection dataset for firearm-related actions
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[5G-IANA] UC1 - Data processed by the AI object detection algorithm
<p>Bounding boxes of objects detected by the artificial intelligence algorithm for each video frame received.</p>
Synthetic Training Dataset for Real-World Terminal Strip Object Detection
<p>This dataset provides synthetic training data for the real-world industrial application of terminal strip object detection to investigate the sim-to-real generalization performance of modern object detectors based on state-of-the-art image synthesis methods. It consists of 30.000 randomly generated synthetic images of terminal strips covering 36 different terminal blocks in five colors and additional accessories such as plug-in bridges, test adapters, end covers and markings. Except from the markings and the DIN rail all objects of the terminal strips are labeled with a bounding box and the respective object class for supervised learning. Additionally, 300 real images of terminal strips were taken and manually labeled for the real-world test.</p> <p>If you use this datset for your research, please consider citing this: <a href="https://arxiv.org/abs/2403.04809">Investigation of the Impact of Synthetic Training Data in the Industrial Application of Terminal Strip Object Detection</a></p>
Calibration of AlgoRithm for Detection of Cardiac Decompensation Via Parametric Objects (CARDCOP)
ClinicalTrials.gov study NCT06661161. IPD Sharing: YES. Countries: 1. Publications: 0.
ForClearingDet - Image dataset of annotated RGB images for object detection in forestry clearing operations
<p>Image dataset of 4 annotated forestry object classes (tree trunks, rocks, vegetation and humans) for object detection during forestry clearing operations. The imbalance of the object class "human" in this dataset was compensated for by the utilisation of the dataset available at <a href="https://www.kaggle.com/datasets/karthika95/pedestrian-detection">https://www.kaggle.com/datasets/karthika95/pedestrian-detection</a>.</p>
Improvement of Lightweight Small Object Ship Detection Network Based on YOLOv7-tiny 模型文件
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Early, Objective Detection of Autism.
ClinicalTrials.gov study NCT06447285. IPD Sharing: NO. Countries: 0. Publications: 0.
Deep Learning for Real Time 3D Multi-Object Detection, Localization, and Tracking : Application to Smart Mobility
<p>In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOV3 [1] which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 [2,3] for monocular vision and MADNEt [4] for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time condition. Object tracking is necessary in order to mitigate the risks of collisions. Unlike, traditional tracking approaches which requires target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT [5] approach for 3D object tracking. We introduce an extended Kalman filter [6] to better estimate the position of objects. Extensive experiments carried out on KITTI dataset [7] prove that our proposal outperforms state-of-the-art approches. </p>
Dataset of the Floating Objects Detection Notebook
<p>This dataset contains the data used in the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery", published in the ocean modelling section of The Environmental Data Science Book.</p>
Glasses Object Detection (OD)
<p>Glasses Object Detection</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.