Detection of morphology defects in pipeline based on 3D active stereo omnidirectional vision sensor

There are many kinds of defects in pipes, which are difficult to detect with a low degree of automation. In this work, a novel omnidirectional vision inspection system for detection of the morphology defects is presented. An active stereo omnidirectional vision sensor is designed to obtain the texture and depth information of the inner wall of the pipeline in real time. The camera motion is estimated and the space location information of the laser points are calculated accordingly. Then, the faster region proposal convolutional neural network (Faster R-CNN) is applied to train a detection network on their image database of pipe defects. Experimental results demonstrate that system can measure and reconstruct the 3D space of pipe with high quality and the retrained Faster R-CNN achieves fine detection results in terms of both speed and accuracy.