Traffic Road Lane and Sign Detection

The detection of traffic road lanes and traffic signs represents a fundamental operation within intelligent transportation systems for autonomous vehicles and advanced driver assistance systems (ADAS). A research study analyses road lane and traffic sign detection through computer vision methods that process authentic video footage. The vehicle implements Lane detection through Canny edge detection and Hough Transform and uses colour segmentation in the HSV colour space to identify traffic signs. According to the experimental results, these traditional image processing techniques successfully detect lane lines and traffic indicators when facing normal operating conditions. The road conditions affected by varying illumination hidden objects and worn-down markings show that better advanced neural network approaches should be developed. Research findings reveal that traditional methodologies should be improved through deep learning models particularly YOLO and Faster R-CNN to produce more accurate and real-time detection results. The research results support developments in intelligent traffic surveillance systems and automated driving systems, which are actively under development.