real time INTELLIGENT turret tracking system using lightweight CNNs: Design & simulation of a AI enabled deep learning for defense systems

The present work explores the design, development, and evaluation of an intelligent real-time turret tracking system that uses lightweight deep learning models for automated defense surveillance. It explains how modern combat environments demand faster, more accurate threat detection systems that can operate with minimal human intervention. The work focuses on applying efficient computer vision techniques, particularly YOLO-based convolutional neural networks, to detect and track multiple targets such as drones, vehicles, and personnel in dynamic scenes. Special attention is given to deploying these models on low-power edge platforms, addressing challenges related to latency, computational limits, and reliability. The book also discusses the creation and use of synthetic simulation environments to train and validate models when real-world military data is limited. Through experimental analysis and performance evaluation, it demonstrates how model optimization, data augmentation, and resolution scaling improve detection accuracy while maintaining real-time operation. Overall, the proposed work provides a practical and technical guide to building AI-driven surveillance systems.

Pagina's: 68, Paperback, LAP LAMBERT Academic Publishing