Vehicle Type Classification using Lightweight CNN from Aerial Images for Traffic Management Applications

In applications related to traffic management, a specific kind of vehicle recognition is important. This research aims to improve traffic management systems by designing and implementing a lightweight Convolutional Neural Network (CNN) for vehicle-type detection from aerial photos. This study aims to develop a model that is accurate in classification and computationally efficient to provide real-time processing skills required for dynamic traffic monitoring. It does this by employing a dataset consisting of high-resolution aerial images taken by drones. The main issue that needs to be addressed is how cars appear differently depending on the angles, sizes, and environmental factors present in aerial imagery. The lightweight CNN architecture is specifically designed to balance performance and computational efficiency, which is critical for implementation in real-time traffic management applications, including low-power devices such as the Raspberry Pi. It optimizes parameter counts and employs approaches that speed up training without sacrificing accuracy. The study's key findings show that the suggested model outperforms pretrained models in terms of both accuracy and efficiency. The model achieves a testing accuracy of 99.31 % while remaining compact, making it ideal for real-time applications.