External pipe weld inspection is a crucial responsibility in today's industrial manufacturing. Traditionally, the image to be detected is generated by a camera that takes images from different angles of the outer pipe and then analyses the detection frame by frame, generating a large amount of data and leading to duplicate identification. In this paper, we propose an algorithm for stitching together a panorama of external pipe welds by first acquiring a sequence of images taken 360° around the same scene, then correcting the image of the pipe column surface by the column surface inverse projection algorithm, then matching the feature points by the Flann algorithm to complete the alignment of the two images, and finally seamlessly stitching and fusing the aligned images to obtain a panorama of external pipe welds. The experimental results show that the algorithm can effectively, quickly and accurately generate a panoramic stitched image of the external pipe weld column surface, which can further provide input for weld quality analysis.
Natural images often suffer from common problem of poor resolution and low SNR. However, conventional methods can’t accurately detect the edge for this type of images. In this paper, we present a robust multi-scale edge detection algorithm for noisy images. Firstly, we down-sample the image to multi-scale resolution, and then the edge features are exacted with the improved difference eigenvalue algorithm. Finally, the multi-scale edges are combined to the accurate edge using the method of Improved New Edge Direction Interpolation (INEDI). Experimental results show that the proposed method outperforms the conventional methods while suppressing noise and preserving edge thus is suited for nature images edge detection.
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