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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Road Feature Detection for Advance Driver Assistance System Using Deep Learning.

Hamza Nadeem^1,2, Kashif Javed², Zain Nadeem^1,2

¹Engineering and Management Sciences, Balochistan University of Information Technology Engineering & Management Sciences, Quetta 87300, Pakistan.

Sensors (Basel, Switzerland)

|May 13, 2023

Summary

This summary is machine-generated.

This study uses computer vision to detect road features, improving driver awareness and preventing accidents. The developed models achieve state-of-the-art accuracy, paving the way for advanced driver assistance systems and self-driving cars.

Keywords:

Driver Assistance Faster-RCNNs YOLOv7 computer vision deep learning object detection traffic signs

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Computer Vision
Artificial Intelligence
Road Safety Engineering

Background:

Road accidents result in numerous injuries and fatalities annually.
Driver inattentiveness to road features like vehicles, pedestrians, and signs is a major contributing factor.
Timely awareness of road features can significantly reduce accident rates.

Purpose of the Study:

To propose a computer vision-based system for detecting and recognizing traffic elements and signs.
To enhance driver awareness and contribute to the development of self-driving cars.
To establish a benchmark for improving traffic safety and enabling advanced automotive technologies.

Main Methods:

A custom, real-world roadside dataset was collected under diverse environmental conditions.
Two deep learning models, YOLOv7 and Faster RCNN, were trained on the annotated dataset.
The models were evaluated for their ability to detect road features.

Main Results:

YOLOv7 achieved a mean Average Precision (mAP) of 87.20%, and Faster RCNN achieved 75.64%.
Both models demonstrated class accuracies exceeding 98.80%.
The results represent a state-of-the-art performance for the task.

Conclusions:

The proposed computer vision models offer a robust solution for real-time road feature detection.
This technology serves as a foundation for developing advanced driver assistance systems (ADAS) and autonomous vehicles.
The study highlights the potential of AI in significantly enhancing road safety and transportation.