Research on Integrating Deep Learning-Based Vehicle Brand and Model Recognition into a Police Intelligence Analysis Platform

This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests involving illicit vehicles reveal that, even in cases of reassigned, concealed, or missing license plates, the model can rely on exterior body features to effectively identify vehicles, reducing dependence on plate-specific data. In practical law enforcement scenarios, these findings can accelerate investigations of stolen or forged plates and enhance overall accuracy. In conclusion, continued collection of vehicle images across broader model types, production years, and modification levels—along with refined annotation processes and parameter adjustment strategies—will further strengthen the method’s applicability within law enforcement intelligence platforms, facilitating more precise and comprehensive vehicle recognition and control in real-world operations.