Visual Change Detection Using Multiscale Super Pixel

The use of autonomous drones in industrial inspection is gaining momentum with improvements in hardware and control. Considering the availability of historical data as drones gather information by regular sorties, a new opportunity for change detection is emerging for inspection and maintenance. In this paper, we propose a visual change detection framework using multi- scale super pixel approach. A framework that can (i) automatically capture appropriate images that match the stored images in the data agnostic drone store, and (ii) perform visual change detection is proposed. The algorithm is tested on a street-view change detection dataset containing images from 152 categories. Image matching step resulted in an accuracy of over 90%. On the street-view dataset, a change detection rate of over 90% is achieved with 60% categories detecting more than 60% of changed region. The proposed approach achieves better performance compared to other state-of-the- art methods that use image descriptors. The capability is implemented on an off-the-shelf drone to demonstrate the utility.