{"url":"/dataset/dronevehicle","name":"DroneVehicle","full_name":"VisDrone-DroneVehicle","description_markdown":"The DroneVehicle dataset consists of a total of 56,878 images collected by the drone, half of which are RGB images, and the resting are infrared images. We have made rich annotations with oriented bounding boxes for the five categories. Among them, car has 389,779 annotations in RGB images, and 428,086 annotations in infrared images, truck has 22,123 annotations in RGB images, and 25,960 annotations in infrared images, bus has 15,333 annotations in RGB images, and 16,590 annotations in infrared images, van has 11,935 annotations in RGB images, and 12,708 annotations in infrared images, and freight car has 13,400 annotations in RGB images, and 17,173 annotations in infrared image. This dataset is available on the download page.\r\n\r\nIn DroneVehicle, to annotate the objects at the image boundaries, we set a white border with a width of 100 pixels on the top, bottom, left and right of each image, so that the downloaded image scale is 840 x 712. When training our detection network, we can perform pre-processing to remove the surrounding white border and change the image scale to 640 x 512.","description_withheld":null,"homepage":"https://github.com/VisDrone/DroneVehicle","introduced_date":"2020-03-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/drone-based-rgbt-vehicle-detection-and","title":"Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning","first_author":"Yiming Sun","url":null},"license":null,"modalities":[],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"}],"languages":[],"variants":["DroneVehicle"],"data_loaders":[],"num_papers_in_archive":18,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/2d-object-detection-on-dronevehicle","task":"2D Object Detection","dataset_variant":"DroneVehicle","rows":11,"metrics":["test/mAP50","test/mAP","Val/mAP50"],"first_row_in_archive_order":{"model":"OAFA","paper":"/paper/weakly-misalignment-free-adaptive-feature","metrics":{"test/mAP50":"79.4"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/weakly-misalignment-free-adaptive-feature","title":"Weakly Misalignment-free Adaptive Feature Alignment for UAVs-based Multimodal Object Detection","date":"2024-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mathbf-c-2-former-calibrated-and","title":"$\\mathbf{C}^2$Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection","date":"2023-06-28","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/translation-scale-and-rotation-cross-modal","title":"Translation, Scale and Rotation: Cross-Modal Alignment Meets RGB-Infrared Vehicle Detection","date":"2022-09-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/target-aware-dual-adversarial-learning-and-a","title":"Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection","date":"2022-03-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/drone-based-rgbt-vehicle-detection-and","title":"Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning","date":"2020-03-05","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/the-cross-modality-disparity-problem-in","title":"Weakly Aligned Cross-Modal Learning for Multispectral Pedestrian Detection","date":"2019-01-09","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cian-cross-image-affinity-net-for-weakly","title":"CIAN: Cross-Image Affinity Net for Weakly Supervised Semantic Segmentation","date":"2018-11-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multispectral-deep-neural-networks-for","title":"Multispectral Deep Neural Networks for Pedestrian Detection","date":"2016-11-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":1,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":7,"samples_ran":1,"samples_unverified":6,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}