Multimodal Object Detection dataset
- Citation Author(s):
-
Siyu Wang
- Submitted by:
- Siyu Wang
- Last updated:
- DOI:
- 10.21227/xh2f-kh37
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Abstract
VEDAI: The VEDAI dataset comprises 1246 high-resolution RGB and infrared images, containing 3640 objects categorized into 8 common vehicle classes. Each image, with a resolution of 1024 × 1024 pixels, spans diverse terrains and environments. Notably, vehicles occupy only a small portion of the image pixels, making small object detection particularly challenging.
DroneVehicle: The DroneVehicle dataset is a large-scale RGB-IR (visible-light and infrared) multimodal dataset designed for vehicle detection in drone imagery. It encompasses a variety of shooting conditions and scenarios, covering different perspectives, altitudes, and lighting conditions. The dataset includes five vehicle categories: car, truck, bus, van, and freight-car.
Instructions:
VEDAI: The VEDAI dataset comprises 1246 high-resolution RGB and infrared images, containing 3640 objects categorized into 8 common vehicle classes. Each image, with a resolution of 1024 × 1024 pixels, spans diverse terrains and environments. Notably, vehicles occupy only a small portion of the image pixels, making small object detection particularly challenging.
DroneVehicle: The DroneVehicle dataset is a large-scale RGB-IR (visible-light and infrared) multimodal dataset designed for vehicle detection in drone imagery. It encompasses a variety of shooting conditions and scenarios, covering different perspectives, altitudes, and lighting conditions. The dataset includes five vehicle categories: car, truck, bus, van, and freight-car.
