A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
نویسندگان
چکیده
Using a safety belt is crucial for preventing severe injuries and fatalities during vehicle accidents. In this paper, we propose real-time occupant detection system based on the YOLOv7 (You Only Look Once version seven) object algorithm. The proposed approach aims to automatically detect whether occupants of have buckled their belts or not as soon they are detected within vehicle. A dataset purpose was collected annotated validation testing. By leveraging efficiency accuracy YOLOv7, achieve near-instantaneous analysis video streams, making our suitable deployment in various surveillance automotive applications. This paper outlines comprehensive methodology training model using labelImg tool annotate with images showing occupants. It also discusses challenges detecting seat evaluates system’s performance real-world dataset. evaluation focuses distinguishing status between two classes: “buckled” “unbuckled”. results demonstrate high level accuracy, mean average precision (mAP) 99.6% an F1 score 98%, indicating effectiveness identifying status.
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ژورنال
عنوان ژورنال: Algorithms
سال: 2023
ISSN: ['1999-4893']
DOI: https://doi.org/10.3390/a16090400