AI-Enhanced Image Processing for Target Detection and Threat Recognition in Defence Applications

The importance of rapid and precise threat recognition has become critical in modern defence operations. In this research, we describe an AI-based image processing framework optimized for real-time detection and recognition of complex military threats. The system integrates satellite, UAV, thermal, and night vision images into a single processing pipeline which performs noise reduction, contrast enhancement as well as normalization. The context focus approach entails extraction of interest regions using motion capture techniques. For object detection and classification, the framework employs advanced deep learning models such as YOLOv8 and Faster R-CNN, with powerful feature extractors ResNet and EfficientNet. Custom CNNs process infrared images for drone, vehicle, weapon and personnel detection surmounted by low visibility conditions. Threat behaviour recognition making use of hybrid CNN-LSTM models utilizes spatial and temporospatial motion interplay. Pose estimation aids detecting aggression or concealed weaponry. Terrain comprehension deepens alongside camo differentiation through semantic segmentation employing DEeplabV3+ and U-Net. The use of Kalman Filters in multi-sensor fusion reduces the rate of false positives while improving tracking accuracy. Our system offers a reliable solution for AI-powered surveillance in defence contexts, undergoing evaluation by measuring operational performance through defined KPIs such as Precision, Recall, IoU, and FPS.