Side Profile Vehicle Binary Images

This is a dataset of vehicle's binary images obtained with an optical barrier. The main goal off this dataset is to train and test a vehicle classification method. Categories are defined according to the number of axles and the presence of double wheel or not. (See the vehicle_categories.png) This dataset is composed by 3 folders. Train: Images used to train a CNN. (Over 1000 images/cat) Total of 11232 images. Experiment1: Images used to validate.5389 images. Experiment2: Field test. Four days off images. 196018 images. Train and Experiment1 are selected images from the same lane, in different periods over 3 months. Experiment2 has images from several different lanes, and 3 different toll stations. We do not delete any image in this set.

本数据集为通过光电屏障（optical barrier）采集得到的车辆二值图像数据集，核心目标为训练并测试车辆分类方法。分类依据车辆轴数以及是否带有双轮进行定义（详见vehicle_categories.png）。本数据集包含3个文件夹：训练集（Train）：用于卷积神经网络（Convolutional Neural Network, CNN）训练的图像，单分类图像数量超1000张，总规模为11232张；实验集1（Experiment1）：用于模型验证的图像，共计5389张；实验集2（Experiment2）：实地测试数据集，采集时长为4天，总图像量达196018张。训练集与实验集1的图像均采自同一车道，采集周期跨越3个月且覆盖不同时段；实验集2的图像则采自多条不同车道以及3个不同收费站，本数据集未对任何图像进行删除处理。