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Automated driving in public traffic still faces many technical and legal challenges. However, automating vehicles at low speeds in controlled industrial environments is already achievable today. A reliable obstacle detection is mandatory to prevent accidents. Recent advances in convolutional neural network-based algorithms have made it conceivable to replace distance measuring laser scanners with common monocameras.
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The data collection was carried out over several months and across several cities including but not limited to Quetta, Islamabad and Karachi, Pakistan. Ultimately, the number of images collected as part of the Pakistani dataset were, albeit in a very small quantity. The images taken were also distributed across the classes unevenly, just like the German dataset. All the 359 images were then manually cropped to filter out the unwanted image background data. All the images were sorted into folders with names corresponding to the label of the images.
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The dataset contains 2,400 vehicle images for license plate detection purposes. Images are taken from actively operating commercial cameras which are installed on a highway and in an entrance of a shopping mall. Images
contain generally one vehicle, but sometimes can contain two or more vehicles. For each image in pixel domain there exists two different images generated from encoded High Efficiency Video Coding (HEVC) stream using our method.
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