Methodology for Image Analysis in Airborne Search and Rescue Operations

Nowadays, Search and Rescue operations can be performed using manned or unmanned Aerial Vehicles. In this latter case, compact cameras are mounted onboard and a bird’s eye view is available to find the missing person. However, the analysis of the video frames can be very challenging and dull for the operators. In this context, the use of graphical methodologies can boost the searching operations and improve the process. In this study, a methodology based on the object detector Yolov5 is introduced: the performances in detecting small objects such as persons in aerial images are evaluated. These algorithms implement shallow layers of the feature extractor to increase the spatial-rich features and help the detector to find small objects. Finally, detection algorithms are tested using a video simulating a scenario for Search and Rescue operations. The filtering of frames containing false positives, is carried out using a classical graphical tool such as the Hamming distance.