Abstract

In the recent times, there has been a lot of speculation related to advanced driver-assistance system (ADAS) which provides best driving experience for the drivers. ADAS technology helps to detect the unhealthy driving conditions which lead to road accidents today. Road accidents are basically caused due to distracted driving, over speeding, drink and drive, foggy weather, no proper headlights, or due to some object which suddenly trespasses the vehicle. Today the major advancements in ADAS include parking assistance, road traffic detection, object detection on highways, and lane detection. But the major risk limitation in ADAS system is the speed and time at which the object is detected and tracked. Several algorithms such as Yolov3, Yolov4, Yolov5,Yolov8 were used for effective object detection and tracking earlier, but sometimes, the system do fail to detect due the speed factor. Hence, the proposed work presents a novel approach called “A Real-Time Object Detection Framework for Advanced Driver Assistant Systems” by implementing the state-of-the-art object detection algorithm which improves the speed in detection of object over real-time. This paper provides a comparison between other state-of-the-art object detectors such as YOLO and YOLO. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three benchmark datasets collected as a part of research findings. YOLO proves to be faster and 95% accurate than the other object detection algorithms in the comparison. This framework is used to build a mobile application called “ObjectDetect” which helps users make better decisions on the road. “Object Detect” consists of a simple user interface that displays alerts and warnings.

Keywords :
dataset ,
Yolov3,
Yolov4,
Yolv5,
Yolov8,
Yolov6

Objective:
For real-time object detection in the context of Advanced Driver Assistance Systems (ADAS) or Autonomous Driving Assistance Systems, the primary objective is to ensure the safety and efficiency of the vehicle by accurately identifying and tracking objects in the environment. Here are key objectives and considerations for implementing real-time object detection in ADAS. comparing five algorithm which has the highest accuracy. of multiple algorithms to address accuracy concerns and enhance the overall performance of the real-time object detection system for ADAS.