Object detection using deep learning methods: A Review | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Articles in Press, Accepted Manuscript, Available Online from 08 April 2023 | ||
| Document Type: Research Paper | ||
| Author | ||
| asmaa alrubaie* | ||
| Al-Esraa University Collage | ||
| Abstract | ||
| 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. Numerous aspects of daily life already include AI, including software for predictive analysis, self-driving cars, and face recognition. Object detection is a key area in AI that draws a lot of interest. One-and two-stage object detection algorithms, the two depend on DL techniques, make up the majority of the present popular object detection algorithms. The key difference between these two approaches is whether or not a region proposal is generated. The generation of a region proposal is not necessary for one-stage object detection algorithms. It has immediate access to the object's coordinate position and classification accuracy. Prior to classifying and locating them, two-stage object detection algorithms must produce a region proposal .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,,; ,،,؛Traditional methods | ||
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