CONVOLUTIONAL NEURAL NETWORKS FOR NEAR REAL-TIME OBJECT DETECTION FROM UAV IMAGERY IN AVALANCHE SEARCH AND RESCUE OPERATIONS

In recent years, unmanned aerial vehicles (UAVs) have been widely used for civilian remote sensing applications. One of them is to assess damages due to man-made or natural disasters and search for bodies in the debris. In this work, we propose to support avalanche search and rescue (SAR) operation with UAVs. The image acquired by the UAV is processed through a pre-trained convolutional neural network (CNN) to extract discriminative features. A linear support vector machine (SVM) is integrated at the top of the CNN to detect objects of interest. Experimental results show encouraging detection performance at a reasonable processing time.