HUMAN DETECTION IN SEARCH AND RESCUE OPERATIONS USING EMBEDDED ARTIFICIAL INTELLIGENCE

Abstract

The paper discusses the use of unmanned aerial vehicles (drones) in search and rescue operations to detect humans in disaster areas where rescue teams cannot reach. The paper highlights the limitations of current methods, including high computational power, high cost, and dependence on internet connectivity. The paper proposes using transfer learning to develop a human detection model with a mean average precision (mAP@0.5) above 90% and compares two deep learning models, MobileNet v2 and EfficientDet. The study uses multi-datasets of aerial images of humans, namely SeaDronesee and SARD, and the TensorFlow version 2.8 framework. MobileNet v2 required less GPU usage for training and yielded a relatively high accuracy of 95.5%, while EfficientDet achieved higher accuracy (97.3%). The trained MobileNet v2 model size is compressed using quantization from 25.5 MB to 4.15 MB, making it suitable for deployment on an edge device for on-chip inference. The paper concludes that the proposed method can improve the efficiency and effectiveness of search and rescue operations.