US 11,790,518 B2
Identification of defect types in liquid pipelines for classification and computing severity thereof
Jayavardhana Rama Gubbi Lakshminarasimha, Bangalore (IN); Mahesh Rangarajan, Bangalore (IN); Rishin Raj, Bangalore (IN); Vishnu Hariharan Anand, Bangalore (IN); Vishal Bajpai, Bangalore (IN); Vishwa Chethan Dandenahalli Venkatappa, Bangalore (IN); Pradeep Kumar Mishra, Bangalore (IN); Gourav Singh Jat, Bangalore (IN); Meghala Mani, Bangalore (IN); Gangadhar Shankarappa, Bangalore (IN); Dinesh Sasidharan Nair, Bangalore (IN); Shashank Lipate, Bangalore (IN); Vineet Lall, Bangalore (IN); Kavita Sara Mathew, Bangalore (IN); Karthik Seemakurthy, Bangalore (IN); and Balamuralidhar Purushothaman, Bangalore (IN)
Assigned to Tata Consultancy Services Limited, Mumbai (IN)
Filed by Tata Consultancy Services Limited, Mumbai (IN)
Filed on Jun. 24, 2021, as Appl. No. 17/357,210.
Claims priority of application No. 202021032479 (IN), filed on Jul. 29, 2020.
Prior Publication US 2022/0036541 A1, Feb. 3, 2022
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06V 10/44 (2022.01); G01N 21/88 (2006.01); G06T 5/00 (2006.01); G06T 7/136 (2017.01); G06T 7/168 (2017.01); G06V 10/25 (2022.01); G06T 5/20 (2006.01)
CPC G06T 7/0006 (2013.01) [G01N 21/8851 (2013.01); G06T 5/007 (2013.01); G06T 5/20 (2013.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06T 7/168 (2017.01); G06V 10/25 (2022.01); G06V 10/443 (2022.01); G01N 2021/8877 (2013.01); G01N 2021/8893 (2013.01); G06T 2207/10016 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A processor implemented method, comprising:
receiving, via one or more hardware processors, an input data comprising at least one of a video data, and one or more images from an image capture device, wherein the input data is specific to a liquid pipeline of a transmission pipeline type, a distribution pipeline type;
extracting, via the one or more hardware processors, one or more optimum frames from the input data specific to the liquid pipeline upon estimating parameters including average luminescence level, a contrast level, and a blur index;
dehazing the one or more extracted optimum frames to obtain one or more dehazed images by extracting one or more local properties of opacity and air-light for each pixel in the extracted optimum frames, wherein the extraction of the local properties results in obtaining an opacity map that is smoothened to extract haze free images;
identifying, from the one or more dehazed images, one or more identified liquid banks and generating one or more contours based on the one or more identified liquid banks;
detecting a change in a liquid level from the one or more generated contours;
estimating a pose of the image capturing device based on (i) an angle of intersection, (ii) a segment intersection, and (iii) a generated circle obtained from the one or more generated contours using a visual sensor in confined noisy environment, and the segment intersection is calculated using angle of intersection and the generated circle obtained from one or more generated contours;
identifying a first set of objects in the liquid pipeline using the estimated pose;
identifying one or more defects in the liquid pipeline based on the first set of identified objects; and
classifying the one or more defects into one or more categories, wherein the step of classifying including identifying region of interests (ROIs) and correcting image pose considering rotation of the image capturing device, wherein once the pose of the image capturing device is corrected, (i) diameter of detected or identified ROIs and (ii) projection angle of junction(s) or connection (s) are calculated followed by classifying the ROI as either a junction or a connection using heuristics based on material, size, and orientation of the first set of objects in frame(s).