基于三维主动立体全向视觉传感器的管道形态缺陷检测外文文献翻译、中英文翻译、外文翻译.doc

Detection of morphology defects in pipeline based on 3D active stereo omnidirectional vision sensor Abstract 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. 1 Introduction Pipeline is widely used to transport crude oil, natural gas, refined oil product, CWM etc. However, an increasing number of these pipes become destroyed by ageing and internal damage comes to existence. If the damage grows large, serious accidents might occur leading to the heavy loss. For the fact that most pipes have a diameter of 700?mm or less, manual vision inspectors cannot enter these pipes directly. Therefore, automatic inspection by robots which overcome spatial constraint is feasible to measure the pipe and great effort has been done in this domain to improve the real time, accuracy, and robustness of the inspection algorithm. Considerable work has been done in non‐destructive detection and visual‐based method for detection and modelling of deformations/dents in structures such as pipes. Non‐destructive detection, for example ultrasound detection [ 1], magnetic flux leakage detection [ 2], eddy current detection and ray detection [ 3], is the branch of engineering concerned with non‐contact