Optimized Vehicle Recognition Using Convolutional Neural Networks: A Deep Learning Approach for Real-World Vehicle Detection Framework

Vehicle detection is an essential task that has many important applications, such as traffic monitoring, autonomous driving, and surveillance systems. In this paper, we implemented an efficient and robust vehicle detection using Convolutional Neural Networks. Utilization of a deep CNN architecture trained on 10,000 images of different vehicles, with the evaluation of mAP that reached 92.5% on a challenging test set of 2,000 images. The runtime is real-time with an image throughput of 30 Frames Per Second (FPS) on a commodity GPU. Methodology: Additionally, varying CNN models and optimization methods affect detection speed and accuracy. Trained on data as recent as jan 2025, results show that CNNs can be used to detect vehicles accurately and efficiently in the real world. Using mAP @0 5 of 94.3% and a [email protected] of 78.6% has been held-out test set of 3,000 images. It accomplished a real-time processing rate of 45 Frames Per Second (FPS) on an NVIDIA GeForce RTX 3080 GPU, making it feasible for utilization in actual work. In addition, evaluation of the effect of data augmentation techniques, including random cropping, flipping, and color jitter, resulted in [email protected] gains of 3.2%. The model was also combined with MATLAB's Computer Vision Toolbox to process video streams in order to distinguish vehicles in real time. Result: The system accomplished 25 FPS detection speed on 1080p content powered by a GPU setup. Furthermore, the models were capable of magnificently detecting the vehicles under different intricate situations like obstructions, variable illumination, and traffic density variability, resulting in a mean Intersection over Union (mIoU) of 0.92. This proposed CNN-based vehicle detection system achieves high accuracy and real-time performance, as demonstrated by the results.