Alon Oring

Alon Oring

The usage of Unmanned Aerial Vehicles (UAVs) in the context of structural health inspection is recently gaining tremendous popularity. Camera mounted UAVs enable the fast acquisition of a large number of images often used for mapping, 3D model reconstruction, and as an assisting tool for inspectors. Due to the number of images captured during large scale UAV surveys, a manual image-based inspection analysis of entire assets cannot be efficiently performed by qualified engineers. Additionally, comparing defects to past inspections requires the retrieval of relevant images which is often impractical without extensive metadata or computer-vision-based algorithms. In this paper, we propose an end-to-end method for automated structural inspection damage analysis. Using automated object detection and segmentation we accurately localize defects, bridge utilities and elements. Next, given the high overlap in UAV imagery, points of interest are extracted, and defects are located and matched throughout the image database, considerably reducing data redundancy while maintaining a detailed record of the defects. Our technique not only enables fast and robust damage analysis of UAV imagery, as we show herein, but is also effective for analyzing manually acquired images.

Alon Oring, Zohar Yakhini, Yacov Hel-Or

Autoencoders represent an effective approach for computing the underlying factors characterizing datasets of different types. The latent representation of autoencoders have been studied in the context of enabling interpolation between data points by decoding convex combinations of latent vectors. This interpolation, however, often leads to artifacts or produces unrealistic results during reconstruction. We argue that these incongruities are due to the structure of the latent space and because such naively interpolated latent vectors deviate from the data manifold. In this paper, we propose a regularization technique that shapes the latent representation to follow a manifold that is consistent with the training images and that drives the manifold to be smooth and locally convex. This regularization not only enables faithful interpolation between data points, as we show herein, but can also be used as a general regularization technique to avoid overfitting or to produce new samples for data augmentation.