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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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FGSC: Fuzzy Guided Scale Choice SSD Model for Edge AI Design on Real-Time Vehicle Detection and Class Counting.

Ming-Hwa Sheu¹, S M Salahuddin Morsalin¹, Jia-Xiang Zheng¹

¹Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu 64002, Taiwan.

Sensors (Basel, Switzerland)

|November 13, 2021

Summary

This study introduces a Fuzzy Guided Scale Choice (FGSC)-based SSD model for efficient vehicle detection and counting. The optimized deep neural network achieves real-time traffic monitoring with high accuracy using edge AI.

Keywords:

and intelligent AIoT vehicles application fuzzy guided scale choice fuzzy logic fuzzy sigmoid function vehicle class counting vehicle detection

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Area of Science:

Computer Vision
Artificial Intelligence
Deep Learning

Background:

Vehicle detection and counting are crucial for intelligent traffic management systems.
Existing methods often face challenges with real-time processing and accuracy, especially in complex environments.

Purpose of the Study:

To develop an optimized deep neural network for accurate vehicle detection and class counting.
To enhance real-time processing capabilities for traffic monitoring applications.

Main Methods:

Proposed a Fuzzy Guided Scale Choice (FGSC)-based Single Shot Detector (SSD) deep neural network architecture.
Integrated FGSC blocks into convolutional layers to emphasize essential features and optimize parameters.
Incorporated a Fuzzy Sigmoid Function (FSF) to expand the activation interval.

Main Results:

Achieved real-time processing speeds of 38.4 Frames Per Second (FPS) on edge AI devices.
Attained an accuracy rate exceeding 94% for vehicle detection and counting.
Demonstrated reduced computational complexity and improved performance efficiency.

Conclusions:

The FGSC-SSD model offers a significant improvement for traffic monitoring using edge AI.
The approach enables efficient and accurate real-time vehicle analysis.
This work contributes to the advancement of intelligent transportation systems.