Detection algorithm of defects on polyethylene gas pipe using image recognition
Aiming at the defects in the polyethylene (PE) gas pipeline, a defect detection algorithm based on image recognition is proposed. Firstly, the collected image is preliminarily screened to obtain the image with defects. Gamma correction algorithm is used to enhance the image, and then dual filtering is used to eliminate the interference of noise on image recognition. Then the defect edge is extracted by the improved traditional Sobel edge detection algorithm. Image defects are segmented by adaptive threshold method, and defect contours are effectively extracted through an open operation. Finally, the defect feature parameters are extracted and inputted into an optimized support vector machine for defect recognition. The experimental results show that the detection algorithm in this paper can effectively improve the observability of the image. Through the trained support vector machine, it can effectively identify the open joint, crack, and deformation defects. The recognition rate of pipeline image defects reached 96.3%.
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