Color-Based Auto-Rickshaw Detection Using Convolutional Neural Networks for Intelligent Transportation Systems

The investigation in this paper is centered on a new methodology for improving the effectiveness of intelligent transportation systems by identifying and distinguishing auto-rickshaws using Convolutional Neural Networks (CNN). The strategy proposed involves training a CNN model to automatically recognize and differentiate the colors of auto-rickshaws, which in turn significantly enhances the detection capabilities. Using hierarchical convolution feature models, the CNN algorithm independently extracts relevant features from images, thereby aiding in precise object detection. An assessment of the model's performance is conducted using a carefully selected dataset containing 1000 auto-rickshaw images, illustrating a high level of accuracy with minimal margin of error. This research makes a valuable contribution to the advancement of automated transport detection within the realm of traffic management. It presents a more streamlined and impactful methodology aimed at improving the overall efficiency and reliability of the system.