The Chinese City Parking Dataset (CCPD) is a dataset for license plate detection and recognition. It contains over 250k unique car images, with license plate location annotations.
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The application-oriented license plate (AOLP) benchmark database has 2049 images of Taiwan license plates. This database is categorized into three subsets: access control (AC) with 681 samples, traffic law enforcement (LE) with 757 samples, and road patrol (RP) with 611 samples. AC refers to the cases that a vehicle passes a fixed passage with a lower speed or full stop. This is the easiest situation. The images are captured under different illuminations and different weather conditions. LE refers to the cases that a vehicle violates traffic laws and is captured by roadside camera. The background are really cluttered, with road sign and multiple plates in one image. RP refers to the cases that the camera is held on a patrolling vehicle, and the images are taken with arbitrary viewpoints and distances.
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This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.
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This dataset aims at evaluating the License Plate Character Segmentation (LPCS) problem. The experimental results of the paper Benchmark for License Plate Character Segmentation were obtained using a dataset providing 101 on-track vehicles captured during the day. The video was recorded using a static camera in early 2015.
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This dataset, called RodoSol-ALPR dataset, contains 20,000 images captured by static cameras located at pay tolls owned by the Rodovia do Sol (RodoSol) concessionaire, which operates 67.5 kilometers of a highway (ES-060) in the Brazilian state of Espírito Santo.
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The ChineseLP dataset contains 411 vehicle images (mostly of passenger cars) with Chinese license plates (LPs). It consists of 252 images captured by the authors and 159 images downloaded from the internet. The images present great variations in resolution (from 143 × 107 to 2048 × 1536 pixels), illumination and background.
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Click to add a brief description of the dataset (Markdown and LaTeX enabled).
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The Caltech Cars dataset consists of 126 rear-view photographs captured within parking lots. These images possess a resolution of 896 × 592 pixels, featuring a solitary vehicle as the primary subject. The acquisitions were made during daylight hours employing a handheld camera at roughly equivalent distances for all instances.
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CD-HARD comprises 102 images featuring vehicles with oblique license plates sourced from the Cars dataset. Each image within this dataset exclusively depicts a single vehicle and was captured during daylight hours. While the dataset encompasses images from diverse geographic regions, it predominantly consists of images seemingly taken in European locales.
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The CLPD dataset comprises 1200 images that encompass various regions within mainland China. These images were sourced from diverse origins, including the internet, mobile devices, and in-car recording devices. While the majority of the images were recorded during daylight hours, a portion of them were captured at nighttime. The dataset predominantly features passenger cars, with a limited number of images depicting trucks and buses.
Vehicle-Rear is a novel dataset for vehicle identification that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates.
A high-quality, balanced dataset of 330,000 images featuring various types of Chinese license plates. The dataset is generated using Generative Adversarial Networks (GANs), ensuring excellent image quality and a balanced distribution of different license plate types. This dataset is perfect for training and evaluating license plate recognition models.
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