Topology-Preserving Deep Hashing for Ultrafast Drone-Dominated Object Detection.

Abstract

Drone (or unmanned aerial vehicle) has been extensively applied in many modern artificial intelligence systems in the past decade. In this work, we propose a novel deep hashing framework that can detect objects from drone-captured pictures extremely fast. Our method can intrinsically and flexibly encode various topological structures from each target object, based on which multiscale objects can be discovered in a view- and altitude-invariant way. Moreover, by leveraging $l_{F}$ and $l_{1}$ norms collaboratively, the calculated hash codes are robust to low-quality drone pictures and possibly contaminated semantic labels. More specifically, for each drone picture, we extract visually/semantically salient object parts inside it. To characterize their topological structure, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary matrix factorization (MF) is designed to hierarchically exploit the semantics of these graphlets, wherein three attributes: 1) deep binary hash codes learning; 2) contaminated pictures/labels denoising; and 3) adaptive data graph updating are seamlessly incorporated. Accordingly, a manifold-regularized feature selector is adopted to further obtain more discriminative deep hash codes. Finally, the selected hash codes corresponding to graphlets within each drone photograph are utilized for ranking-based object discovery. Comprehensive experiments on the DAC-SDC, MOHR, and our self-compiled dataset have demonstrated the competitive speed and accuracy of our method.