Image Detecting Technology Underground Pipeline Location Distribution

As the step of urban planning and construction speeds up, construction and maintenance of underground pipelines is playing an indispensable role. Before the construction of the pipeline, constructors must fully ascertain the laying, position and burial depth of the underground pipelines around the construction site. Currently, in engineering applications, constructors mainly use the pipeline detectors, ground penetrating radar and other equipment to get the relevant parameters of the pipeline’s positional distribution and draw pipeline distribution maps. However, in actual measurement, these methods have some problems such as weak anti-interference ability, workload and low positioning accuracy. This paper presents a new method which combines computer vision detection technology with ARM embedded processing technology to detect the position of the pipeline based on the optical measurement principle of the Sweden Reflex Maxibor borehole inclinometer. This method achieves the position detection of the underground pipeline by calculating the three-dimensional position of the ring which is in the front of the CMOS image sensor. It can achieve automatic detection which reduces the workload and testing costs.The underground pipeline position detection system mainly includes image acquisition subsystem, camera calibration subsystem, target detection and position subsystem and data processing subsystem. Image acquisition subsystem uses OV9650camera as the capture device, this paper designs and writes the OV9650device drivers and uses interrupt, semaphores and wait queue mechanism which Linux provides to solve the problem of the concurrency control of equipment. User application completes the image data acquisition of the circle. Generally, the camera has lens distortion effect, so this paper analyzes the image model of the camera and uses Zhang calibration method to calibrate the OV9650camera in camera calibration subsystem. Then, this paper uses the intrinsic parameters and distortion parameters to correct these images. This operation can improve the measurement accuracy of our system. After correction these images, this paper uses image filter, threshold segmentation, high light elimination, contour extraction and other image processing algorithms to calculate the position information of the target circle. Data processing subsystem can use the position information to reconstruct the shape and orientation of the pipeline.