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Summary

This study introduces an improved YOLOv4 algorithm for enhanced small object detection, particularly for license plate recognition. The AI-powered system achieves real-time performance on embedded platforms, even with challenging image conditions.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Object detection algorithms are crucial in computer vision, powered by AI and ML.
Existing models like YOLOv4 excel but struggle with detecting small objects.
License plate recognition is a key application area for small object detection.

Purpose of the Study:

To propose an improved YOLOv4 network structure for accurate small object detection.
To develop an algorithm for real-time, high-resolution small object detection on embedded systems.
To integrate an auditory speech signal for detecting fake license plates.

Main Methods:

Enhanced YOLOv4 network structure utilizing image decomposition into low and high-frequency features.
Classifier training using a positive dataset derived from core image patterns.
Real-time data processing with speech alerting signals for detected objects.

Main Results:

Achieved real-time detection speeds of 45 fps at 1280x960 resolution.
Demonstrated high accuracy in detecting tilted, blurred, and occluded license plates.
Significantly reduced computational load while maintaining comparable accuracy.

Conclusions:

The proposed algorithm offers an effective solution for small object detection, especially in license plate recognition.
The system is highly applicable for autonomous driving and auditory traffic monitoring.
Integration of speech alerts enhances functionality for detecting suspicious license plates and reducing criminal activity.