Research On Cableless Pipeline Detection Robot Based On Digital Image Processing Technology

In general industry,military equipment,nuclear facilities,oil,gas and other fields,the pipeline has been widely used as a common device of material delivery.Pipeline inspection robots play an important role in the routine inspection and maintenance of pipelines.Among them,the cableless pipe inspection robot has the advantages of long transmission distance,convenient use and so on.So the cableless pipe inspection robot has a very broad application prospect.In this paper,the hardware circuit and software system of robotic control system are researched and designed.Experiments show that the performance of the control system is stable.Based on the collected pipe pictures,a set of algorithms for automated pipe defect detection and classification based on digital image processing technology are integrated.Experiments show that the algorithm has a high degree of automation,versatility and accuracy.The main contents of this paper are as follows:1.The wireless data transmission scheme of the cableless pipe inspection robot is designed.The wireless transceiver nRF24L01 is used to transmit the control commands and the sensor information.The WIFI technology is used to transmit the video information.The research and design of the hardware circuit system of the cableless pipeline inspection robot includes the STM32F103ZET6 chip,the minimum system,the related peripheral circuits and the sensor module.2.The software control system of the cableless pipeline inspection robot is designed.PC software is written in C # software language for image display,robot motion control and the processing of sensor information.The cos II?operating system is transplanted in the lower computer to facilitate the real-time scheduling of the multitask.On this basis,the wireless communication program,attitude sensor program,wet and temperature sensor program,motor and servo control program and the communication protocol are designed.Experiments show that the wireless data transmission scheme of the control system is feasible.3.A set of algorithm scheme about automated pipe defect detection and classification based on digital image processing technology is integrated.First,image enhancement,image segmentation,mathematical morphology and boundary tracking are used for image preprocessing.Then,after extracting the size,shape and texture features of the defective area,we choose the circularity,convexity,eccentricity,entropy,correlation and cluster tendency as the feature vector.Finally,K-means clustering analysis based on particle swarm optimization and statistical pattern recognition classifier are used to classify.Using the image preprocessing algorithm in this paper,we can successfully extract the pipe defects and achieve the purpose of automated pipe defect detection.K-means clustering analysis based on particle swarm optimization successfully clusters the pipe defect images into three categories which are crack defects,pipe joint defects and hole corrosion respectively.Compared with the traditional K-means algorithm,clustering accuracy is increased by 9%,16.7% and 12.5% respectively.The clustering analysis based on particle swarm optimization,the statistical pattern recognition classifier and neutral network classifier are used to classify the pipe defects.Finally,classification results of classifiers are compared.