ON THE USE OF DEEP NEURAL NETWORKS FOR THE DETECTION OF SMALL VEHICLES IN ORTHO-IMAGES

Jean Ogier Du Terrail (1, 2) , Frédéric Jurie (1)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 SAFRAN Electronics & Defense

This paper addresses the question of the detection of small targets (vehicles) in ortho-images. This question differs from the general task of detecting objects in images by several aspects. First, the vehicles to be detected are small, typically smaller than 20x20 pixels. Second, due to the multifarious-ness of the landscapes of the earth, several pixel structures similar to that of a vehicle might emerge (roof tops, shadow patterns, rocks, buildings), whereas within the vehicle class the inter-class variability is limited as they all look alike from afar. Finally, the imbalance between the vehicles and the rest of the picture is enormous in most cases. Specifically, this paper is focused on the detection tasks introduced by the VeDAI dataset [1]. This work supports an extensive study of the problems one might face when applying deep neural networks with low resolution and scarce data and proposes some solutions. One of the contributions of this paper is a network severely outperforming the state-of-the-art while being much simpler to implement and a lot faster than competitive approaches. We also list the limitations of this approach and provide several new ideas to further improve our results.

Mots clés: target detection; deep-learning; aerial imagery

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Jean Ogier Du Terrail, Frédéric Jurie. ON THE USE OF DEEP NEURAL NETWORKS FOR THE DETECTION OF SMALL VEHICLES IN ORTHO-IMAGES. IEEE International Conference on Image Processing, Sep 2017, Beijing, China. ⟨hal-01527906⟩