Cover
Vol. 20 No. 1 (2024)

Published: June 30, 2024

Pages: 149-159

Original Article

Multiple Object Detection-Based Machine Learning Techniques

Athraa S. Hasan 1 Jianjun Yi 2 Haider M. AlSabbagh 3 Liwei Chen
1Department of Mechanical Engineering, East China University of Science and Technology ECUST-Shanghai 200237, China
2Department of Electromechanical, University of Technology, Baghdad, Iraq
3Department of Electrical Engineering, College of Engineering, University of Basrah, Basrah, Iraq
History
  • Received: April 22, 2023
  • Revised: June 4, 2023
  • Accepted: June 28, 2023
  • Available Online: January 31, 2024
DOI

https://doi.org/10.37917/ijeee.20.1.15

Abstract

Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of 97%.

Keywords

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