Real-time vehicle type recognition using a convolution and Haar wavelet pooling based classifier

Over the past few years, real-time classification of vehicle types has become an increasingly popular and important topic, given its wide range of applications in traffic control and analysis. Among the various methods available for classifying car types, convolutional neural networks (CNNs) have emerged as particularly appealing. In this article, we introduce a new real-time CNN architecture that is specifically designed to detect different types of cars. This innovative structure incorporates several unique features, including novel network architecture and structural elements, as well as an advanced learning method based on the back-propagation algorithm. One key aspect of our proposed method is its use of feature extraction at three different locations within the network, which allows for more accurate and efficient classification of car types. To evaluate the performance of our approach, we conducted experiments on two popular datasets (IRVD and MIO-TCD), and compared our results against those obtained using traditional CNN structures. Our evaluation results demonstrate that our proposed CNN architecture outperforms existing approaches, achieving superior classification accuracy across a range of criteria. Overall, our work represents a significant advance in the field of real-time car type classification, with broad implications for traffic management and analysis.