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