Design and implementation of real-time object detection system based on single-shoot detector and OpenCV

Frontiers in Psychology

article

Fazal Wahab¹  Inam Ullah²  Anwar Shah³  Rehan Ali Khan⁴  Ahyoung Choi⁵  Muhammad Shahid Anwar⁵ 

[1] College of Computer Science and Technology, Northeastern University;BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University;School of Computing, National University of Computer and Emerging Sciences;Department of Electrical Engineering, University of Science and Technology;Department of AI and Software, Gachon University

关键词: Computer Vision;    deep learning;    Image Recognition;    object detection;    object recognition;    Single Shoot Detector;   

DOI  :  10.3389/fpsyg.2022.1039645

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Computer vision (CV) and human-computer interaction (HCI) are essential in many technological fields. Researchers in computer vision are particularly interested in real-time object detection techniques, which have a wide range of applications, including inspection systems. In this study, we design and implement real-time object detection and recognition systems using the Single Shoot Detector (SSD) algorithm and deep learning techniques with pre-trained models. The system can detect static and moving objects in real-time and also recognize the object's class. The primary goals of this research were to investigate and develop a real-time object detection system that employs deep learning and neural systems for real-time object detection and recognition. In addition, we evaluated the free available, pre-trained models with the SSD algorithm on various types of data sets to determine which models have high accuracy and speed when detecting an object. Moreover, the system is required to be operational on reasonable equipment. We tried and evaluated several deep learning structures and techniques during the coding procedure and developed and proposed a highly accurate and efficient object detection system. This system utilizes freely available datasets such as MSCOCO, PASCAL Voc, and Kitti. We evaluated our system's accuracy using various metrics such as precision and recall. The proposed system achieved high accuracy of 97% while detecting and recognizing real-time objects.

【 授权许可】

CC BY