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.本研究自主构建了一款涵盖指定军用与民用载具类别的数据集。该数据集总计包含6772张图像，涉及军用卡车(Military Truck)、主战坦克(Military Tank)、军用战机(Military Aircraft)、军用直升机(Military Helicopter)、民用轿车(Civilian Car)及民用航空器(Civilian Aircraft)六大目标类别。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，即上述六大类目标；负样本图像则指仅包含非预设目标的图像。所有正样本图像中共计包含11528个待检测目标。引入负样本图像的核心目的在于，使模型能够学习图像中无待检测目标的场景分布。为避免因图像格式不统一增加模型处理难度，我们将所有图像的格式从.jpeg和.png统一转换为.jpg格式。完成数据集采集与预处理流程后，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需求。本数据集的标注文件支持.txt、.csv、.xml以及TFRecord四种格式。