Internal thread defect detection system of petroleum tubing based on multi-vision
Pipe thread detection in the petroleum industry faces challenges such as narrow space,insufficient light and geometric complexity.Traditional manual detection methods have low efficiency and high error rate.Therefore,this paper proposes an automatic internal thread detection scheme based on machine vision,aiming to improve the detection accuracy and efficiency.By optimizing the image acquisition system,the image acquisition speed and quality are significantly improved,ensuring the acquisition of clear thread images when the light conditions are poor.At the same time,the cylindrical model stitching technology is used to synthesize multiple thread images into a full-field view,which enhances the comprehensiveness of the detection.In terms of image processing,the system combined with YOLOv8 deep learning model can quickly and accurately locate and classify thread defects.The research results show that the technology performs well in a narrow space,provides an efficient and reliable solution,meets the needs of the oil pipe tool industry for efficient and accurate detection,significantly improves the safety and reliability of pipe tools,and has a wide range of application prospects.
万方数据
维普
