Lwando Nkuzo

Mr. Lwando Nkuzo

Electrical, Electronic, and Computer Engineering, Central University of Technology, Bloemfontein, Free State, South Africa

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 August 2023 in Algorithms
Verified
Journal Article
Open access
PDF file
Lwando NkuzoLwando Nkuzo
Lwando Nkuzo
MSmalusi sibiya
malusi sibiya
EMElisha Markus
Elisha Markus
Using a safety belt is crucial for preventing severe injuries and fatalities during vehicle accidents. In this paper, we propose a real-time vehicle occupant safety belt detection system based on the YOLOv7 (You Only Look Once version seven) object detection algorithm. The proposed approach aims to automatically detect whether the occupants of a vehicle have buckled their safety belts or not as soon as they are detected within the vehicle. A dataset for this purpose was collected and annotated for validation and testing. By leveraging the efficiency and accuracy of YOLOv7, we achieve near-instantaneous analysis of video streams, making our system suitable for deployment in various surveillance and automotive safety applications. This paper outlines a comprehensive methodology for training the YOLOv7 model using the labelImg tool to annotate the dataset with images showing vehicle occupants. It also discusses the challenges of detecting seat belts and evaluates the system’s performance on a real-world dataset. The evaluation focuses on distinguishing the status of a safety belt between two classes: “buckled” and “unbuckled”. The results demonstrate a high level of accuracy, with a mean average precision (mAP) of 99.6% and an F1 score of 98%, indicating the system’s effectiveness in identifying the safety belt status.