Enhance Efficiency of QR Code Detection on Movement Object: Performance Analysis of Algorithms and Environmental Factors

Abstract

This paper investigates the enhancement of QR code detection efficiency on moving objects, with a focus on analyzing the performance of different algorithms and environmental factors affecting detection accuracy. As QR codes have become a standard tool for information access and digital identification, optimizing their readability under various conditions, particularly on moving platforms, is crucial. The primary objective of this study is to explore and evaluate the impact of object speed on QR code detection efficacy, considering both algorithmic approaches and external influences such as lighting, angle of incidence, and background noise. The scope of this research encompasses the real-time detection of QR codes affixed to vehicles, aiming to understand how motion dynamics influence recognition performance and to propose effective solutions for improving the accuracy of QR code scanning under such conditions. The experimental setup involved recording video files of a car with a QR code displayed on its windshield, driven at varying speeds to test the detection system's robustness. Using this method, the researchers aimed to simulate real-world scenarios where QR codes are scanned from moving vehicles, such as toll booths, smart parking systems, and transportation logistics. The analysis reveals that the QR code recognition system demonstrated a 100% success rate at vehicle speeds below 30 kilometers per hour, confirming the system’s effectiveness in low-motion conditions. However, the detection accuracy significantly declined to 30% when the car’s speed increased to 60 kilometers per hour, highlighting the challenges posed by high-motion environments and motion blur. This degradation suggests that while current QR code detection algorithms are effective under stable or low-motion scenarios, their performance is critically impacted by higher velocities, necessitating further research into motion-compensating algorithms and enhanced image processing techniques. The novelty of this research lies in its practical approach to quantifying the impact of motion on QR code detection accuracy and providing a detailed performance analysis of detection algorithms under varying speeds, offering insights into the limitations and potential areas for improvement in the field of mobile QR code scanning. By employing this empirical method, the result was accurate detection under controlled conditions, thereby providing a foundation for developing robust solutions capable of maintaining high recognition rates even at increased speeds. The study’s findings not only contribute to the understanding of how motion affects QR code readability but also serve as a guideline for future advancements in algorithm development and system optimization for mobile QR code applications.