Transfer Learning and CNN-Based Vehicle Identification Approach for Hit-and-Run Cases

In the swiftly evolving landscape of the modernized transportation, characterized by unprecedented technological advancements, the escalating frequency of Hit-and-Run incidents on the road poses a critical challenge to the people's well being. In response, we present a pioneering vehicle identification approach leveraging Transfer Learning-enhanced Convolutional Neural Networks (TL-enhanced CNN) to address the Hit-and-Run cases which can endanger the people' life. By harnessing the power of continuous traffic monitoring, camera sensors, and location data, the proposed approach adeptly detects and classifies vehicles involved in Hit-and-Run incidents. This model is trained on diverse vehicular images and the TL-enhanced CNN methodology not only identifies vehicle types, but also extracts essential information within the dynamic monitoring environment. The core of proposed approach lies in an Ensembled CNN model, combining the strengths of Visual Geometry Group (VGG-16), Residual Network (ResNet-50) and InceptionV3, enhancing both accuracy and robustness. Rigorous evaluation, encompassing training parameter analysis, loss curves, accuracy curves, and comprehensive performance metrics, establishes the robustness and efficacy of this proposed approach, thus offering a potent solution to the mounting challenge of Hit-and-Run detection and response in modern transportation systems.