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%.

针对聚乙烯气体管道中的缺陷，提出了一种基于图像识别的缺陷检测算法。首先，对收集的图像进行初步筛选以获得具有缺陷的图像。使用伽玛校正算法对图像进行增强，然后使用双重滤波消除噪声对图像识别的干扰。然后通过改进的传统Sobel边缘检测算法提取缺陷边缘。通过自适应阈值方法对图像缺陷进行分割，并通过开放操作有效地提取缺陷轮廓。最后，提取缺陷特征参数并将其输入到优化的支持向量机中以进行缺陷识别。实验结果表明，本文提出的检测算法可以有效地提高图像的可观察性。通过训练有素的支持向量机，它可以有效地识别开缝，裂纹和变形缺陷。管道图像缺陷识别率达到96.3％。