AI-driven Real-time System for Land Surveillance and Reconnaissance

Detection of threat elements during dangerous land missions such as rescue operations, bomb disposal, surveillance and reconnaissance using an unmanned ground vehicle (UGV) plays a significant role in technological warfare. Accurate and rapid detection of enemy arsenal and threats can help better plan and deploy military armaments while greatly reducing human casualty and economic losses. Unmanned ground systems with good maneuverability, superior wireless communication, and powerful multi-sensor and data processing capabilities can significantly advantage over the enemies. One of the key data processing tasks, i.e., land-warfare threat detection, is developed and tested in this paper. The goal is to identify and differentiate between threats in a warfare scenario. Four such threats have been defined and used to develop the detection algorithm for the study: soldiers, tanks, tents, and helicopters. Different object detection algorithms (ODAs) such as Single Shot Detector (SSD), CenterNet and Faster R-CNN based on pre-trained Convolutional Neural Networks were tested and compared. COCO evaluation metrics were used as performance parameters to evaluate each detection algorithm on the different threats selected. The results show that CenterNet ODA performs better in evaluation and inference when compared to other ODAs, obtaining the highest mean Average Precision of 85.89% and 93.75%, respectively, at 0.5 IoU with Resent101 V1 CNN architecture. The best trade-off between all performance parameters was obtained again using CenterNet Resent101 V1 FPN. The inference was tested on a Raspberry Pi-based UGV streamed data. It was concluded that such systems have a key role in future warfare with a strong communication system and a lighter version of ODA. Further, researchers and engineers can use the work achieved in this paper to develop robust detection and data processing models and incorporate it into various applications and domains.