Iterative Deep Homography Estimation

Abstract

We propose Iterative Homography Network, namely IHN, a new deep homography estimation architecture. Different from previous works that achieve iterative refinement by network cascading or untrainable IC-LK iterator, the iterator of IHN has tied weights and is completely trainable. IHN achieves state-of-the-art accuracy on several datasets including challenging scenes. We propose 2 versions of IHN: (1) IHN for static scenes, (2) IHN-mov for dynamic scenes with moving objects. Both versions can be arranged in 1-scale for efficiency or 2-scale for accuracy. We show that the basic 1-scale IHN already outperforms most of the existing methods. On a variety of datasets, the 2-scale IHN outperforms all competitors by a large gap. We introduce IHN-mov by producing an inlier mask to further improve the estimation accuracy of moving-objects scenes. We experimentally show that the iterative framework of IHN can achieve 95% error reduction while considerably saving network parameters. When processing sequential image pairs, IHN can achieve 32.7 fps, which is about 8× the speed of IC-LK iterator. Source code is available at https://github.com/imdumpl78/IHN .

An Ensemble Learning Aided Computer Vision Method with Advanced Color Enhancement for Corroded Bolt Detection in Tunnels

“…However, the traditional CV algorithms require the manual design of filter modules, which has poor robustness and low accuracy. Deep learning-based CV bolt corrosion detection becomes available for engineering as deep learning develops [9][10][11]. For instance, Cha et al [12] developed an autonomous structural visual inspection method via Region-based Convolutional Neural Networks (RCNNs) for real-time damage detection covering concrete cracks, steel and bolt corrosion, and steel delamination.…”