Hybrid object detection methodology combining altitude-dependent local deep learning models for search and rescue operations

Abstract

Due to their detection capabilities and low cost, unmanned aerial vehicles (UAVs) are commonly used in search and rescue (SAR) operations. In a SAR mission, the UAV's height may change, causing objects to shrink or increase thus affecting generalization. This paper proposes a hybrid object detection (OD) technique that combines altitude-dependent local deep learning (DL) models, each one designed for a given flight altitude range. Seven cutting-edge OD models, including YOLOv4 and v5, EfficientDet, Detectron2, MobileNet, and Faster R-CNN, were trained locally with YOLOv5 and Scaled YOLOv4 being the best performers in low and high-altitude images, respectively. The suggested hybrid strategy, which uses the best OD performers, outperformed well-known DL algorithms with 86.2% mAP on a public dataset. Computing efficiency and accuracy with images of varying resolutions were also explored. Dividing the fundamental detection issue into local subproblems that are treated separately by powerful OD networks might increase SAR capabilities.