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International Journal of Nonlinear Analysis and Applications، جلد ۱۳، شماره ۲، صفحات ۱۴۹۵-۱۵۰۴
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عنوان انگلیسی A survey of deep learning-based object detection: Application and open issues
چکیده انگلیسی مقاله Object tracking and detection are among the most significant jobs in computer vision, having many applications in areas, which includes autonomous vehicle tracking, robotics, as well as traffic monitoring. Several studies have been conducted in past years. However, since detecting various problems, for instance, fast motion, illumination variations, as well as occlusion, study in this field persists. Furthermore, deep convolutional neural networks (DCNNs) have grown increasingly significant for object detection as deep learning (DL) techniques have advanced. As a result, numerous approaches for object detection are studied in this research, as well as a comprehensive. This project encompasses backbone networks, loss functions and training strategies, classical object detection architectures, complex problems, datasets and evaluation metrics, applications, future development directions, as well as a review and analysis of DL-based object detection techniques conducted in previous years. Experts in the field of object detection will benefit from this review article.
کلیدواژه‌های انگلیسی مقاله Convolutional Neural Networks, Deep learning, machine learning, object detection
نویسندگان مقاله Shaymaa Tarkan Abdullah |
Department of computer science, College of science, University of Diyala, Baqubah, Iraq

Bashar Talib AL-Nuaimi |
Department of computer science, College of science, University of Diyala, Baqubah, Iraq

Hazim Noman Abed |
Department of computer science, College of science, University of Diyala, Baqubah, Iraq

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_6525_414aebf2df9ccefc59f352ed1201ab78.pdf
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