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89,747 Images Vehicle Attributes Annotation Data. The data includes outdoor roads (highway, crossroad) scenes. The data covers multiple vehicle types, multiple vehicle colors, multiple license plate colors, multiple vehicle brands, different time, different vehicle orientations. For a vehicle, 2 rectangular bounding boxes and 5 labels were annotated, the rectangular bounding boxes include the front end or rear end of vehicle, and the whole vehicle; the labels included vehicle color, vehicle type, vehicle brand, vehicle orientation and shooting time; For a vehicle plate, a rectangular bounding box of license plate, the number of license plate and the color of license plate were annotated. This data can be used for tasks such as vehicle attributes analysis and license plate recognition.
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Specifications
Data size
89,747 images, including 26,504 images of speeding scenes and 63,243 images of retrograde scenes
Collecting environment
outdoor roads (highway, crossroad)
Data diversity
including multiple vehicle types, multiple vehicle colors, multiple license plate colors, multiple vehicle brands, different time，different vehicle orientations
Device
surveillance camera, looking down angle
Collecting time
day, night
Image parameter
the image format is .jpg, the annotated file format is .json
Annotation content
for a vehicle, 2 rectangular bounding boxes and 5 labels were annotated, the rectangular bounding boxes include the front end or rear end of vehicle, and the whole vehicle; the labels included vehicle color, vehicle type, vehicle brand,vehicle orientation and shooting time; for a vehicle plate, a rectangular bounding box of license plate, the number of license plate (the number of license plate was replaced with six ‘*’) and the color of license plate were annotated
Accuracy
a rectangular bounding box is qualified when the deviation is not more than 3 pixels, and the qualified rate of the rectangular bounding box shall not be lower than 96%; the accuracy of labels of license plate number annotation, license plate color, vehicle color, vehicle type, vehicle brand, shooting time and vehicle orientation is not less than 96%
Sample
Waiting For Data
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46,498 Images - Vehicle Damage Images Collection Data. The dataset diversity includes multiple vehicle types, multiple outdoor scenes, multiple types of vehicle damage, multiple collecting angles, different photographic distances, and different resolutions. The types of vehicle damage include bump, scratch, paint loss and other vehicle damage. The locations of vehicle damage include the front hood, left and right headlights, door, body and trunk of the vehicle. This dataset can be used for tasks such as automatic vehicle damage detection.
multiple vehicle types multiple outdoor scenes multiple types of vehicle damage multiple collecting angles different photographic distances different resolutions
Specifications
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