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Object Detection Using Deep Learning Methods: A Review | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 11, Volume 23, Issue 2, June 2023, Pages 136-152 PDF (1.83 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.33103/uot.ijccce.23.2.11 | ||
| Authors | ||
| Asmaa Hasan Alrubaie* 1; Maisa'a Abid Ali Khodher* 2; Ahmed Talib Abdulameer* | ||
| 1Al-Esraa University Collage | ||
| 2Department of Computer Science, University of Technology, Iraq | ||
| Abstract | ||
| Target detection, one of the key functions of computer vision, has grown in importance as a study area over the past two decades and is currently often employed. In a certain video, it seeks to rapidly and precisely detect and locate a huge amount of the objects according to redetermined categories. The two forms of deep learning (DL) algorithms that are used in the model training algorithm are single-stage and 2-stage algorithms of detection. The representative algorithms for every level have been thoroughly discussed in this work. The analysis and comparison of numerous representative algorithms in this subject is after that explained. Last but not least, potential obstacles to target detection are anticipated. | ||
| Keywords | ||
| Object detection; Deep learning; Regions of interest (ROI); Convolutional Neural Networks )CNNs) | ||
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