Mouheb Kaouther, Yürekli Ali, Yılmazel Burcu
Department of Computer Engineering, Eskişehir Technical University, Eskişehir 26555, Turkey.
Data Brief. 2021 Aug 31;38:107321. doi: 10.1016/j.dib.2021.107321. eCollection 2021 Oct.
In the field of transportation and logistics, smart vision systems have been employed successfully to automate various tasks such as number-plate recognition and vehicle identity recognition. The development of such automated systems is possible with the availability of large image datasets having proper annotations. The TRODO dataset is a rich-annotated collection of odometer displays that can enable automatic mileage reading from raw images. Initially, the dataset consisted of 2613 frames captured in different conditions in terms of resolution, quality, illumination and vehicle type. After data pre-processing and cleaning, the number of images was reduced to 2389. The images were annotated using the CVAT image annotation tool. The dataset provides the following information for each frame: the type of odometer (analog or digital), the mileage value displayed on the odometer, the bounding boxes of the odometer, and the digits and characters displayed on the screen. Combined with machine learning and artificial intelligence, the TRODO dataset can be used to train odometer classifiers, digit recognition and number reading models from odometers and similar types of displays.
在交通运输和物流领域，智能视觉系统已成功应用于实现各种任务的自动化，如车牌识别和车辆身份识别。有了带有适当注释的大型图像数据集，此类自动化系统的开发才成为可能。TRODO数据集是一个对里程表显示屏进行了丰富注释的集合，能够从原始图像中实现自动里程读数。最初，该数据集由2613帧在分辨率、质量、光照和车辆类型等不同条件下捕获的图像组成。经过数据预处理和清理后，图像数量减少到2389张。这些图像使用CVAT图像注释工具进行了注释。该数据集为每一帧提供以下信息：里程表的类型（模拟或数字）、里程表上显示的里程值、里程表的边界框以及屏幕上显示的数字和字符。结合机器学习和人工智能，TRODO数据集可用于训练里程表分类器、数字识别以及从里程表和类似类型显示屏中读取数字的模型。
