Military Vehicle Object Detection System

Abstract: The detection of military vehicles is critical for modern defense systems and surveillance technologies. In this project, we propose a deep learning-based object detection system capable of identifying and classifying military vehicles under realworld constraints such as varied terrain, lighting, and camouflage. Our method incorporates hierarchical feature representation and post-processing strategies including non-maximum suppression. A custom military vehicle dataset (MVD) wasdeveloped fortraining and testing. The proposed system achieves robust real-time detection and can be deployed across a variety of defense applications. The Military Vehicle Object Detection project aims to enhance the situational awareness of military operations by developinganautomatedsystemcapableof detecting, classifying, and tracking military vehicles in real-time. Leveraging advanced deep learning techniques, such asconvolutionalneuralnetworks(CNNs), the system processes live video feeds or static images to accurately identify various types of military vehicles, including tanks and armored personnel carriers. The implementation includes a user-friendly web interface for operators to visualize detection results and access historical data. Additionally, a number platedetectionmoduleenhances security by extracting and verifying vehicle identification information. This project contributes to improved operational efficiencyanddecision-makinginmilitary contexts by facilitating rapid identification and monitoring of military assets