Blockage of culverts by debris material is reported as main cause of urban flash floods. Extraction of blockage information using intelligent video analytic (IVA) algorithms can prove helpful in making timely maintenance-related decisions toward avoiding flash floods. Having known the percentage of visual blockage at culverts can help better prioritise the maintenance of highly blocked culvert sites. This article proposes a deep learning-based segmentation-classification pipeline where visible culvert openings are segmented at the first stage and classified into one of four percentage visual blockage classes at the second stage. Images of Culverts and Blockage (ICOB) and Visual Hydraulics-Lab Blockage Dataset (VHD) dataset were used to train the deep learning models. From the results, Mask R-CNN (ResNet50 backbone) achieved the best segmentation performance (i.e. mAP@75 of 77.2%), while NASNet achieved the best classification performance (i.e. 81.2% test accuracy). To demonstrate the i
AI-Based System Inspects Defects in Wind Turbine Blades More Accurately
Written by AZoRoboticsApr 1 2021
While the demand for wind power has increased considerably, the accompanying need to assess wind turbine blades and recognize flaws may have an impact on their operational efficiency.
Crack detection using the AI tool. Image Credit: Loughborough University.
From ultrasound to visual thermography, a broad range of blade inspection methods have been tested, but these techniques have demonstrated certain limitations.
There are many inspection procedures that still need engineers to perform manual examinations in which a vast number of high resolution images are captured. Such analyses consume time and are affected by light conditions, and added to this, they are also dangerous.