Document Type : Original Research Paper

Authors

• Vincent Andrew Akpan ¹
• David Ayo-oluwa Adegoke ²
• Ebunoluwa Temiloluwa Adejayan ³
• Kehinde Adesola Adepoju ⁴

¹ Department of Biomedical Engineering, School of Electrical Systems Engineering, The Federal University of Technology, P.M.B. 704, Akure, Ondo State, Nigeria.

² Biomedical Engineering Department, Redeemer’s Health Village, Redemption City of God, Mowe, Ogun State, Nigeria

³ Biomedical Engineering Unit, Federal Medical Centre, MMA Road, Owo, Ondo State, Nigeria

⁴ Department of Biomedical Engineering, Lagos State University Teaching Hospital (LUTH), Yaba, Lagos State, Nigeria

Abstract

Background and Objectives: The manual method of writing down vehicle plate numbers (VPNs), vehicle types, date, and time-stamps at the point of entry into and/or exit from the premises of organizations as well as the exit and/or entry time are not only time-consuming and stressful but are also prone to errors, delays, inconsistency and possible loss of hand-written data due to possible environmental hazards which makes this archaic method unreliable especially for security reasons. This study presents an artificial intelligence-based YOLOv3 using DarkNet-53 deep convolutional neural network (CNN) model architecture for the development of an automatic vehicle inventory system (AVIS) with a PostgreSQL-based dynamic relational database system (RDBS) for captured data storage and retrieval in real-time using Google cloud storage/retrieval.
Methods: The AVIS with dynamic RDBS employs power-over-Ethernet (PoE) switch, PoE IP-based camera, Airtel router/Wi-Fi module and YOLOv3 using DarkNet-53 algorithm to capture and process VPNs from streaming video of moving vehicles. The processed results are stored in a properly designed dynamic RDBS over Google cloud storage system. The dynamic RDBS automatically creates and inserts all relevant vehicle information for security surveillance and tracking purposes. Several standard quantitative and qualitative metrics have been used to evaluate the performances of the YOLOv3 using DarkNet-53 architecture against YOLOv8 using CSPDarkNet-53 and YOLOv3 using SqueezeNet model architectures for comparison purposes.
Results: Quantitatively, the YOLOv3 using DarkNet-53 and YOLOv8 using CSPDarkNet-53 achieved virtually equal performance metrics except for the excessive long execution time of 4.5839 hours used by YOLOv8 with CSPDarkNet-53compared to the 2.9713 minutes used by the YOLOv3 with DarkNet-53. The YOLOv3 with SqueezeNet used only 1.9901 minutes with relatively lower performance metric values. Qualitatively, successful and accurate LPNs detection and recognition with dynamic RDBS update to the cloud within 3 seconds for 25 random vehicles entering and/or exiting the premises of a car dealer company for a period of three days between 10:00am and 2:00pm daily has been achieved with YOLOv3 using DarkNet-53 model architecture.
Conclusion: The proposed low-cost AVIS based on YOLOv3 using DarkNet-53 model architecture with a PostgreSQL dynamic RDBS and Google cloud storage have been successfully designed, implemented and validated with successful results for LPN detection and recognition. The proposed techniques offers promising potentials for timely and accurate data collection to optimize vehicle inventory management, control and operations for security surveillance design.

Keywords

• Automatic Vehicle Inventory System (AVIS)
• Computer Vision
• Real-Time Object Detection
• Relational Database System (RDBS)
• You Only Look Once version 3 (YOLO v3)

Main Subjects

• Computer Vision