Machine Learning Techniques for Vehicle Detection | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 1, Volume 22, Issue 4, December 2022, Pages 1-12 PDF (977.9 K) | ||
| Document Type: Research Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.22.4.1 | ||
| Authors | ||
| Maryam Raad Shihab* 1; Rana Fareed Ghani* 2; Athraa Jasim Mohammed* 1 | ||
| 1Computer Sciences Department, University of Technology, Baghdad, Iraq | ||
| 2Department of Computer Science, University of Technology, Iraq-Baghdad | ||
| Abstract | ||
| The traffic surveillance system is a type of intelligent system of traffic control. Traffic control provides solutions to most problems faced by people. It helps to monitor, detect traffic congestion and traffic accidents. As science evolved, it became possible to control traffic using video surveillance. Video surveillance is the most economical option that does not involve high costs or changes in infrastructure. Vehicle detection is one of the main parts of the traffic surveillance system. In this paper, vehicles will be detected using two different artificial intelligence methods (the YOLO method and the HAAR cascade classifier method). The first one is smarter than the second method, and both of them contain machine learning. The first processing step will read the video. Then vehicle detection algorithms are applied using two different ways. The comparison between them depends on the results to find the most effective and applicable vehicle detection method. After implementing the two methods, results were obtained using YOLO, that the accuracy is 91.31% and the error rate is 8.69%, in time 10 sec. As for using the XML (HAAR cascade classifier method) method, the accuracy is 86.9%, the quality is 86.9%, completeness is 90.9%, and the error rate is 13%, in time 17 sec. Thus, we conclude that the YOLO method has better results than the second method. | ||
| Keywords | ||
| Artificial Intelligence; Machine Learning; Vehicle Detection; Yolo Method | ||
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