From concrete to abstract: Multilayer neural networks for disaster victims detection

Abstract

Search-and-rescue (SAR) team main objective is to quickly locate victims in post-disaster scenario. In such disaster scenario, images are usually complex containing highly cluttered background such as debris, soil, gravel, ruined building, and clothes, which are difficult to distinguish from the victims. Previous methods which only work on nearly uniform background taken from either indoor or yard are not suitable and can deteriorate the detection system. In this paper, we demonstrate the feasibility of multilayer neural network for disaster victims detection on highly cluttered background. Theoretical justification from which deep learning learns from concrete to object abstraction is established. In order to build a more discriminative system, this theoretical justification then leads us to perform pretraining using data-rich datasheet followed by finetuning only on the last layers using data-specific datasheet while keeping the other layers fixed. A new Indonesian disaster victims datasheet is also provided. Experimental results show the efficiency of the method for disaster victims detection in highly cluttered background.