Military and Civilian Vehicles Classification

We created our own dataset featuring the required military and civilian vehicle classes. The dataset contains a total of 6772 images of military trucks, military tanks, military aircraft, military helicopters, civilian cars and civilian aircraft. Out of which, 6642 are positive images and 130 are negative images. Positive images are those which contain one or more of the defined objects (i.e. Military Truck, Military Tank, Military Aircraft, Military Helicopter, Civilian Car, Civilian Aircraft). Negative images are those which contain anything else except the defined objects. All the positive images contains a total of 11528 objects. The use of negative images has a specific purpose, which is, to make the model learn about such an environment when there are no detectable objects in the image. The extension of images was converted from .jpeg and .png to .jpg, since, it is difficult to process the models with the different extensions. After the dataset collection and pre-processing, the formation of specific format files is to be carried out for dealing further with the object detection models. The Labelling is done in .txt, .csv, .xml, and tf record formats.

本研究自主构建专属数据集，覆盖预设的军用与民用载具类别。该数据集总计包含6772张图像，涵盖军用卡车、军用坦克、军用航空器、军用直升机、民用轿车及民用航空器六类目标。其中正样本图像6642张，负样本图像130张。正样本图像指包含至少一种预设目标的图像，即军用卡车、军用坦克、军用航空器、军用直升机、民用轿车、民用航空器；负样本图像则指仅包含非预设目标内容的图像。所有正样本图像总计包含11528个目标实例。设置负样本图像具有明确目的：使模型能够学习图像中无可检测目标的场景分布。为避免因图像扩展名不统一给模型训练带来处理困难，我们将所有图像的扩展名从.jpeg与.png统一转换为.jpg。完成数据集采集与预处理后，需生成特定格式的标注文件以适配后续目标检测模型的开发与训练。标注文件采用.txt、.csv、.xml以及TFRecord格式进行存储。