Visionguart:an Adaptive Yolo-driven Driver Assistance Framework For Real-time Road Object Intelligence

Abstract

Road Accidents Continue To Be A Major Global Concern, Often Caused By Delayed Driver Reaction And Limited Awareness Of Surrounding Conditions. Although Modern Vehicles Include Driver Assistance Technologies, Many Existing Systems Struggle To Provide Reliable Real-time Object Detection Under Challenging Environments Such As Low Light, Heavy Traffic, And Adverse Weather. This Paper Presents VISIONGUARD, An Adaptive YOLO-based Driver Assistance Framework Designed To Deliver Real-time Road Object Intelligence. The Proposed System Utilizes The YOLOv8 Deep Learning Model To Detect Vehicles, Pedestrians, Traffic Signs, And Road Obstacles With High Speed And Accuracy. Unlike Traditional Systems That Are Restricted To Predefined Object Categories, The Framework Incorporates Adaptive Detection Mechanisms To Enhance Flexibility In Dynamic Traffic Environments. The System Processes Live Video Input, Extracts Frames Using OpenCV, And Performs Object Detection With Minimal Latency. Risk Assessment Is Conducted To Generate Immediate Visual And Audio Alerts For Drivers. The Framework Is Optimized For Deployment On Low-power Edge Devices, Ensuring Practical In-vehicle Implementation. Experimental Observations Demonstrate Improved Detection Performance And Real-time Responsiveness. The Proposed Solution Contributes Toward Safer And Smarter Transportation Systemsby Enhancingdriver Awareness And Reducing Accident Risks.

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