Sewage Pipeline Fault Detection using Image Processing

This research proposes a ground breaking method for examining the condition of sewage pipes. The underground sewage piping system in cities is a vital form of common infrastructure because it helps to ensure a safe atmosphere. One of the most commonly used sewer inspection process which uses CCTV systems, has a weak performance. A camera is installed on one side of the pipe or on some other unit, and video is recorded within the pipes and sent off-line to an engineer to classify possible faults. The machine-controlled detection and testing of the location of divergences within the internal structure is the subject of this project with the help of Open Source computer vision Library techniques. Many steps are used in the machine-controlled inspection technique, including normalize RGB, Background Subtraction, Canny edge detection, Arc Detect, contours High-light, time Convert, and circular Mask, which involves segmenting the image into Mathematical choices and the defected unit field. Recognizing and classifying defects found within the pipe of an area unit using computation perception techniques helped reconcile image processing. The method of detection is both fast and automatic.