این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
مدیریت فناوری اطلاعات، جلد ۱۴، شماره ۴، صفحات ۴۰-۵۶
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Comparing the Performance of Pre-trained Deep Learning Models in Object Detection and Recognition
چکیده انگلیسی مقاله The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.
کلیدواژه‌های انگلیسی مقاله Deep learning,Image recognition,Object Detection,Pre-trained Models
نویسندگان مقاله Omar Ibrahim Obaid |
Department of Computer Science, College of Education, AL-Iraqia University, Baghdad, Iraq.

Mazin Abed Mohammed |
Ph.D., College of Computer Science and Information Technology, University of Anbar, Ramadi, 31001, Iraq

Akbal Omran Salman |
Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.

Salama A. Mostafa |
Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, 86400, Malaysia.

Ahmed A. Elngar |
Faculty of Computer & Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt; College of Computer Information Technology, American University in the Emirates, United Arab Emirates

نشانی اینترنتی https://jitm.ut.ac.ir/article_88134_0413b598ec2be4d705c24fcab5e0d253.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