Smart Traffic Flow Detection via Canny Edge Detection

Abstract

Current traffic management methods, such as timers or manual control, have been shown to be inefficient in addressing the 
growing issue of urban congestion. This project proposes a novel system that leverages real-time vehicle density analysis through 
Canny edge detection and digital image processing for dynamic traffic control. This automated system significantly enhances 
response time, vehicle flow management, reliability, and overall operational efficiency compared to traditional systems. As urban 
traffic congestion worsens, there is an urgent need to incorporate advanced technologies into traffic control mechanisms. The 
proposed system outlines a comprehensive process from image acquisition to edge detection, culminating in adaptive green signal 
allocation based on varying traffic conditions, demonstrated through four sample images. The effectiveness of this approach is 
validated through hardware implementation, proving its viability in real-world scenarios