System Design Using Sensor Compression and Grey Wolf Optimizer for AI-Based Security Applications

The security of public spaces is a major concern. In particular, there is a need for compact and non invasive concealed threat detection systems. In this paper, we investigate the reduction of the number of sensors using a joint design scheme involving the number of antennas and the Support Vector Machine (SVM) classification algorithm. We have developed a joint optimization that combines a bio-inspired optimized method, called Grey Wolf Optimizer, and the Run-Length Encoding (RLE) compression method. Results are compared to a reference case using 22,801 sensors. The classification performance reaches 75 %, with specificity and sensitivity (that account for lethal objects) values of 66 % and 83 %, respectively. The optimized system reduces the number of sensors to 2,818 with an overall detection performance of 79 %. A sub-sampling method further reduces the system to 1,038 sensors, achieving 100 % sensitivity, guaranteeing detection of all concealed threats.