The Intelligent Transportation System (ITS) seeks to improve traffic flow to guarantee transportation safety. One of the ITS's fundamental tenets is identifying and classifying vehicles into various classes. Although the issues related to small size, variety of forms, and similarity in visual appearance of the vehicles, as well as the influence of the weather on the video and image quality, make it challenging to categorize vehicles using unmanned aerial vehicles (UAV); they are becoming more popular in computer vision-related applications. Traffic accidents are now a serious public health concern that must be addressed in the Kurdistan Region of Iraq. An automatic vehicle detection and classification system can be considered one of the remedies to solve this issue. This paper presents a dataset of 2,160 images of vehicles on the roads in the Iraqi Kurdistan Region to address the issue of the absence of such a dataset. The images in the proposed collection were taken with a Mavic Air 2 drone in the Iraqi cities of Sulaymaniyah and Erbil. The images are categorized into five classes: bus, truck, taxi, personal car, and motorcycle. Data gathering considered diverse circumstances, multiple vehicle sizes, weather and lighting conditions, and massive camera movements. Pre-processing and data augmentation methods were applied to the images in our proposed dataset, including auto-orient, brightness, hue, and noise algorithm, which can be used to build an efficient deep learning (DL) model. After applying these augmentation techniques for the car, taxi, truck, motorcycle, and bus classes, the number of images was increased to 5,353, 1,500, 1,192, 282, and 176, respectively.
Keywords: Data augmentation; Deep learning; Machine learning; UAV; Vehicle classification; Vehicle detection; Vehicle tracking.
© 2024 The Authors. Published by Elsevier Inc.