Pakistani Traffic-sign Recognition Dataset

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. A python script was used to rename the images from 0-359 in alphabetic order of their class labels (so that images from ‘Bridge Ahead’ were named 0-12 whereas images from ‘Zigzag Road Ahead’ were named 346-359). The sizes of these cropped images were non-uniform but, a CNN only accepts input images with uniform dimensions, whatever they might be. Hence, all the images were then resized to the shape of 32 x 32.本次数据采集工作历时数月，覆盖巴基斯坦境内多座城市，涵盖奎达（Quetta）、伊斯兰堡（Islamabad）及卡拉奇（Karachi）等多地。最终，本次巴基斯坦数据集所采集的图像总量偏少。与德国数据集类似，采集所得的图像在各个类别间分布并不均衡。随后，研究人员对全部359张图像进行手动裁剪，以剔除无关的背景图像数据。所有图像均按照其对应标签的名称整理至不同文件夹中。随后使用Python脚本（Python script）按照类别标签的字母顺序将所有图像重命名为0至359号：例如，“前方桥梁（Bridge Ahead）”类的图像编号为0至12，而“前方之字形道路（Zigzag Road Ahead）”类的图像编号则为346至359。经裁剪后的图像尺寸并不统一，但卷积神经网络（CNN）仅能接收尺寸统一的输入图像，无论该统一尺寸具体为何。因此，研究人员将所有图像统一调整为32×32的尺寸。

搜集汇总

背景与挑战

背景概述

该数据集是一个专门针对巴基斯坦交通标志的小规模图像数据集，包含359张经过手动裁剪和调整至32x32像素的图像，收集自奎达、伊斯兰堡和卡拉奇等多个城市。它适用于计算机视觉和机器学习任务，但数据量有限且类别分布不均匀，主要用于交通标志识别研究。