基于小波变换的双阈值水下焊缝图像边缘检测研究

Research on Double-Threshold Edge Detection for Under-Water Welding Seam Image Based on Wavelet Transform

水下焊缝图像受到水下环境、弧光、飞溅、泥沙浑浊等噪声影响,生成的焊缝图像模糊不清质量较差,采用传统的边缘检测算法提取水下焊缝边缘,结果无法满足要求。为解决此问题,利用Canny算法,提出基于多尺度小波变换的自适应双阈值边缘检测算法。该算法结合多尺度小波变换,以三次B样条为小波函数,采用双线性插值进行非极大值抑制,根据OTSU法生成自适应双阈值抑制噪声和去除伪边缘。实验结果表明:相比传统的Canny算法,此改进算法对水下焊缝图像边缘检测效果更佳,边缘检测更精确、丰富完整,且有效抑制噪声,验证了此算法的有效性。

Underwater welding seam image is subjected to effect by noise, such as underwater environment, arc, spatter, sediment and so on, making the image poor quality.If classics edge detection algorithm is used to extract underwater welding seam edge, the result isn’t satisfactory.Canny algorithm was used as the foundation, a auto-adaptive double-threshold edge detection algorithm was proposed based on multiscale wavelet transform.In the algorithm, multiscale wavelet transform was used, cubic B-spline was used as wavelet function, double linear interpolation was used to suppress non-maximum, auto-adaptive double-threshold generated by using OTSU method was used to restrain noise and remove pseudo boundary.The experimental results show that this method is better than the tradition Canny algorithm, the edge detection is more accurate and complete, moreover the noise is restrained effectively.The validity of the algorithm is verified.