Super-linear speedup for real-time condition monitoring using image processing and drones

Real-time inspections for the large-scale solar system may take a long time to get the hazard situations for any failures that may take place in the solar panels normal operations, where prior hazards detection is important. Reducing the execution time and improving the system’s performance are the ultimate goals of multiprocessing or multicore systems. Real-time video processing and analysis from two camcorders, thermal and charge-coupling devices (CCD), mounted on a drone compose the embedded system being proposed for solar panels inspection. The inspection method needs more time for capturing and processing the frames and detecting the faulty panels. The system can determine the longitude and latitude of the defect position information in real-time. In this work, we investigate parallel processing for the image processing operations which reduces the processing time for the inspection systems. The results show a super-linear speedup for real-time condition monitoring in large-scale solar systems. Using the multiprocessing module in Python, we execute fault detection algorithms using streamed frames from both video cameras. The experimental results show a super-linear speedup for thermal and CCD video processing, the execution time is efficiently reduced with an average of 3.1 times and 6.3 times using 2 processes and 4 processes respectively

3D Scene Geometry Estimation from 360∘^\circ Imagery: A Survey

This paper provides a comprehensive survey on pioneer and state-of-the-art 3D scene geometry estimation methodologies based on single, two, or multiple images captured under the omnidirectional optics. We first revisit the basic concepts of the spherical camera model, and review the most common acquisition technologies and representation formats suitable for omnidirectional (also called 360$^\circ$, spherical or panoramic) images and videos. We then survey monocular layout and depth inference approaches, highlighting the recent advances in learning-based solutions suited for spherical data. The classical stereo matching is then revised on the spherical domain, where methodologies for detecting and describing sparse and dense features become crucial. The stereo matching concepts are then extrapolated for multiple view camera setups, categorizing them among light fields, multi-view stereo, and structure from motion (or visual simultaneous localization and mapping). We also compile and discuss commonly adopted datasets and figures of merit indicated for each purpose and list recent results for completeness. We conclude this paper by pointing out current and future trends.Comment: Published in ACM Computing Survey