Dump truck object detection dataset including scale-models

Object detection is a vital part of any autonomous vision system and to obtain a high performing object detector data is needed. The object detection task aims to detect and classify different objects using camera input and getting bounding boxes containing the objects as output. This is usually done by utilizing deep neural networks. When training an object detector a large amount of data is used, however it is not always practical to collect large amounts of data. This has led to multiple different techniques which decreases the amount of data needed. Examples of such techniques are transfer learning and domain adaptation. Working with construction equipment is a time consuming process and we wanted to examine if it was possible to use scale-model data to train a network and then used that network to detect real objects with no additional training. This small dataset contains training and validation data of a scale dump truck in different environments while the test set contains images of a full size dump truck of similar model. The aim of the dataset is to train a network to classify wheels, cabs and tipping bodies of a scale-model dump truck and use that to classify the same classes on a full-scale dump truck. The label structure of the dataset is the YOLO v3 structure, where the classes corresponds to a integer value, such that: Wheel: 0 Cab: 1 Tipping body: 2目标检测是各类自主视觉系统的关键组成部分，打造高性能目标检测器离不开数据集的支撑。目标检测任务旨在基于摄像头输入的图像，对各类目标进行检测与分类，并输出包含目标位置的边界框（bounding box），此类任务通常通过深度神经网络（deep neural network）实现。 在训练目标检测器时，通常需要海量数据，但实际场景中大规模采集数据往往并不具备可行性。为此，学界提出了多种可降低数据需求量的技术手段，例如迁移学习（transfer learning）与域自适应（domain adaptation）。针对工程机械设备的相关研究往往耗时耗力，我们希望探索能否仅使用比例模型数据集训练神经网络，便可直接将其用于检测真实工程机械设备，且无需进行额外训练。 本小型数据集包含不同环境下的比例款自卸卡车的训练与验证数据，测试集则采用同型号全尺寸自卸卡车的图像。本数据集的构建目标为：训练神经网络以识别比例款自卸卡车的车轮、驾驶室与倾卸斗，并将该训练得到的模型直接用于全尺寸自卸卡车的同类目标分类。 本数据集采用YOLO v3的标签结构，类别与整数值的对应关系如下：车轮：0；驾驶室：1；倾卸斗：2。