Method and system for asset inspection using unmanned aerial vehicles

A system (100), for inspecting asset using an unmanned aerial vehicle comprising:

• a memory (102) storing instructions;
• one or more communication interfaces (106);
• and
• one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
• deploy, an unmanned aerial vehicle to inspect one or more assets associated with an asset monitoring environment, wherein the unmanned aerial vehicle is configured to flew on a predetermined height above the one or more assets being inspected within the asset monitoring environment;
• receive, a plurality of input images of an asset from the one or more assets using an image capturing device positioned with the unmanned aerial vehicle, wherein the plurality of input images provides structural health of the asset from the one or more assets being inspected;
• generate, by a 3D point cloud generator, a 3D point cloud based on the overlapped images identified from the plurality of input images of the asset being inspected;
• convert, the 3D point cloud into a plurality of 2D image frames based on the angle of projection from the top view of the asset being inspected;
• generate, a bird's eye view of the asset based on the plurality 2D image frames, wherein the empty patches of each 2D image frame from the plurality 2D image frames are filled based on range-domain filtering;
• detect, one or more objects of interests from the plurality 2D image frames using a mask convolutional network (RCNN);
• detect, by an anomaly detection technique, anomalies present in the one or more objects of interests associated with the plurality 2D image frames, wherein the anomalies are detected based on a training data serving as ground truth associated with the trained convolutional autoencoder, wherein the training data are samples of the one or more objects of interest for determining deviation observed;
• and
• detect, by a change detection technique, defects observed with the one or more objects of interests based on the change observed in each 2D image frame from the plurality 2D image frames in a specific view with an identical objects of interest of the 2D image frame representing the same asset based on varying time stamps and a EXIF data based closest possible pairing.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors perform actions comprising:

• deploying, an unmanned aerial vehicle to inspect one or more assets associated with an asset monitoring environment, wherein the unmanned aerial vehicle is configured to fly at a predetermined height above the one or more assets being inspected within the asset monitoring environment;
• receiving, a plurality of input images of an asset from the one or more assets using an image capturing device positioned with the unmanned aerial vehicle, wherein the plurality of input images provides structural health of the asset from the one or more assets being inspected;
• generating, by a three-dimensional (3D) point cloud generator, a 3D point cloud based on one or more overlapped images identified from the plurality of input images of the asset being inspected;
• converting, the 3D point cloud into a plurality of 2D image frames based on an angle of projection from a top view of the asset being inspected;
• generating, a birds eye view of the asset based on the plurality 2D image frames, wherein one or more empty patches of each 2D image frame from the plurality 2D image frames are filled based on range-domain filtering;
• detecting, one or more objects of interests from the plurality 2D image frames using a mask convolutional network (RCNN);
• detecting, by an anomaly detection technique, one or more anomalies present in the one or more objects of interests associated with the plurality 2D image frames, wherein the one or more anomalies are detected based on a training data serving as ground truth associated with a trained convolutional autoencoder, and wherein the training data comprises one or more samples of the one or more objects of interest for determining deviation observed;
• and
• detecting, by a change detection technique one or more defects observed in the one or more objects of interests based on the change observed in each 2D image frame from the plurality 2D image frames in a specific view with an identical objects of interest of the 2D image frame representing the asset based on varying time stamps and an EXIF data based closest possible pairing.