این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Artificial Intelligence and Data Mining، جلد ۹، شماره ۱، صفحات ۱-۹
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی IRVD: A Large-Scale Dataset for Classification of Iranian Vehicles in Urban Streets
چکیده انگلیسی مقاله In recent years, vehicle classification has been one of the most important research topics. However, due to the lack of a proper dataset, this field has not been well developed as other fields of intelligent traffic management. Therefore, the preparation of large-scale datasets of vehicles for each country is of great interest. In this paper, we introduce a new standard dataset of popular Iranian vehicles. This dataset, which consists of images from moving vehicles in urban streets and highways, can be used for vehicle classification and license plate recognition. It contains a large collection of vehicle images in different dimensions, viewing angles, weather, and lighting conditions. It took more than a year to construct this dataset. Images are taken from various types of mounted cameras, with different resolutions and at different altitudes. To estimate the complexity of the dataset, some classic methods alongside popular Deep Neural Networks are trained and evaluated on the dataset. Furthermore, two light-weight CNN structures are also proposed. One with 3-Conv layers and another with 5-Conv layers. The 5-Conv model with 152K parameters reached the recognition rate of 99.09% and can process 48 frames per second on CPU which is suitable for real-time applications.
کلیدواژه‌های انگلیسی مقاله Vehicle Dataset, Vehicle Classification, deep learning, IRVD
نویسندگان مقاله H. Gholamalinejad |
Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran.

H. Khosravi |
Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran

نشانی اینترنتی http://jad.shahroodut.ac.ir/article_1789_01f6b7f73e9e655b1fcdbf51ed31b8af.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات