In-pipe inspection robotic system for defect detection and identification using image processing

Abstract

Pipe inspection is a crucial process in any industry and it must be regularly done for the smooth and safe functioning of industries. Manual Pipe inspection is a sluggish and labour-intensive task therefore it is recently replaced by using robots. The main aim of the proposed work is to fabricate in pipe inspection robot to detect and identify various pipe abnormalities such as blockage, internal hole, crack, and corrosion on the inner surface of the pipe using image processing (I.P) and machine vision (M.V). The robotic system is cable to move inside the pipe with raspberry camera, processor, LED and other components mounted on it. While in motion inside the pipe, the live surveillance of the environment will be provided. Through machine vision technique, defect detection and identification will be carried out. In this research work, a prototype model is fabricated using a wheel-type mechanism having DC motors. The proposed robotics system is integrated with a Raspberry camera and the training of the model for machine vision is done using a tensor flow environment and python programming. The control of the robot is completely based on the internet of things (IoT) and the live visual inspection is proposed for effective analysis. Image processing through simulation will focus on generating and showing the output of various image processing transformations such as image blurring, image smoothing, converting image to binary, canny image formation, applying logarithmic transform, and image contouring. The proposed results help to determine the present condition of the pipe and help in avoiding pipe failures.