Authors - Viraj Bhatt, Rajvi Bhimani, Bhupendra Fataniya, Dhaval Shah Abstract - Cross-border security remains a critical concern for global stability, particularly in jungle or forested terrains where soldiers face significant risks. Military camouflage is engineered to blend in with natural surroundings using advanced concealment techniques that match local textures and color patterns. Consequently, the identification of concealed threats is a challenging task where human observation is prone to error due to poor visibility and fatigue. Traditional surveillance methods often rely on optical sensors which may fail to efficiently detect modern military camouflage. To address this, an automated detection model was developed using the YOLOv8-Nano architecture and deployed on NVIDIA Jetson Nano hardware. The framework was validated using a 5- fold cross-validation strategy to ensure robust and reliable performance. Experimental results yielded a peak mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5 of 0.955 and an average mAP of 94.8%. The model was further optimized into a TensorRT engine using FP16 quantization, achieving a final footprint of 5.9 MB. These results demonstrate that low-power, portable hardware can effectively perform real-time surveillance as an edge-AI system. This also results in minimizing risks to human lives and directly supporting the core mission of Sustainable Development Goal-16 (SDG-16).
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