Enhancing Vehicle Image Recognition via Cloud Computing and Deep Learning Techniques: A Comprehensive Study

Abstract: This research explores the transformative impact of integrating cutting-edge technology into intelligent transportation systems, specifically focusing on the utilization of cloud computing for vehicle image recognition. The study employs sophisticated deep learning algorithms deployed on cloud platforms to enhance the capabilities of real-time, scalable, and precise vehicle detection. System effectiveness is rigorously evaluated through comprehensive analyses of platform performance and image recognition metrics. The purpose of this research is to compare and contrast three deep learning models' abilities to identify vehicles in images: Inception, Xception, and MobileNet. We assess these models using criteria including loss, accuracy, validation loss, and accuracy in validation, using a large dataset.The models are trained and fine-tuned on a cloud computing platform, leveraging transfer learning from pre-trained ImageNet weights. Our findings reveal that while Inception achieves high training accuracy, Xception and MobileNet demonstrate superior generalization with higher validation accuracies and lower validation losses. Insights gained from this comparative analysis inform recommendations for optimizing model selection and deployment in real-world applications, emphasizing the importance of robust performance metrics in advancing vehicle image recognition technologies. Future research directions include further refining model architectures and exploring ensemble approaches to enhance accuracy and reliability in diverse operational environments.