UGEN: UAV and GAN-aided Ensemble Network for Post-Disaster Survivor Detection through ORAN

Post-disaster scene understanding frameworks are increasingly crucial in Search And Rescue (SAR) operations. Unmanned Aerial Vehicles (UAVs) provide an efficient means to carry out the task of scene understanding due to the higher altitudes at which they function. However, complex environments in post-disaster scenarios make it difficult for UAVs to detect humans or objects accurately. Inefficient object detection mechanisms lead to low accuracy for object detection tasks. Hence, to mitigate these issues, we propose a UAV and GAN-aided Ensemble Network (UGEN) framework for efficient ORAN-based post-disaster survivor detection. This approach deploys a Context-Conditional Generative Adversarial Network (CCGAN)- based model to remove occlusion in the images obtained from the UAVs. The UGEN framework classifies entities present in the visual scope of the UAV using a semantic segmentation framework deployed on the CCGAN-enhanced images, resulting in a pixel-level prediction of entities present in the post-disaster images. An ensemble network comprising a combination of single-stage and multi-stage detectors detects survivors present in the post-disaster scenario, thereby combining the benefits of both architectures, resulting in a reduced false negative rate and improved performance. An Open Radio Access Network (ORAN) executes data propagation between the UAV and the ground station for reduced transmission latency. The proposed model achieved a survivor detection accuracy of 96.7%.