JJEM: Special Edition 2 (December 2024)
JJEM: Special Edition 2 (December 2024)
2024-12-08
CNN and Yolov8m Based Car Classification.
Priyanka K.R, Sunitha G.P.
Vehicle identification based on tail lights involves recognizing vehicles by the unique design and pattern of their tail lights. This method uses distinct visual features to identify specific models. To identifying vehicles by detecting their tail lights. Tail lights signal a vehicle's presence and actions, especially at night or in poor visibility. By using innovative image processing techniques like the HAAR transformation and dee learning, this study aims to accurately identify vehicles based on the head and tail light patterns. This system detects tail lights in real-time, providing important data for autonomous driving, traffic monitoring, and collision avoidance. The effectiveness of Haar Cascade Classifiers and deep learning technique, such as Convolutional Neural Networks (CNNs), is evaluated to improve identification accuracy and speed. As self-sufficient vehicles become more common, reliable tail light detection is important for road safety. By training models on diverse tail light images, the system can identify vehicle make and model build on tail light design in various lighting conditions and angles. Tail lights, important for both function and style, often feature unique shapes and LED arrangements that help in identification. These designs can be characterized by their geometric patterns (like circles, rectangles, or more complex shapes), the distribution and intensity of light.
vehicle, head and Tail light, recognition, Image Processing