Published Paper Details:

Vehicle identification and classification are crucial for applications such as traffic management, security, and automated systems. This study presents a novel approach to vehicle identification and classification using advanced image processing techniques and machine learning algorithms. Specifically, the method employs Convolutional Neural Networks (CNNs) for feature extraction and classification, leveraging deep learning to analyze and differentiate vehicles based on make, model, and other distinguishing characteristics. To enhance the robustness of the system, algorithms such as Histogram of Oriented Gradients (HOG) for feature extraction and Support Vector Machines (SVM) for classification are integrated. These techniques address challenges posed by varying vehicle shapes, sizes, and environmental conditions, improving the accuracy and reliability of the identification system. The proposed method is evaluated against traditional approaches, demonstrating superior performance in terms of precision, robustness, and processing efficiency. This approach not only advances vehicle recognition technology but also supports improved traffic management and security applications, contributing to more effective and efficient transportation systems.