Research on Welding Seam Detection and Recognition Technology for Industrial Boilers

In the production process of industrial boilers, automatic detection and identification of welds is a key task, as the quality of welds directly affects the safety and service life of boilers. In order to improve the accuracy and efficiency of weld seam recognition, a weld seam detection and recognition algorithm based on OpenCV-Python is proposed. Using an improved watershed algorithm for foreground detection and image segmentation, in order to reduce excessive segmentation caused by the watershed algorithm, the gradient function is modified and the gradient image is thresholded to eliminate excessive segmentation caused by small changes in grayscale. So as to separate the weld seam from the background. The advantages of this algorithm are high computational efficiency and strong applicability, which can meet the requirements of machine vision recognition systems. Moreover, the algorithm also incorporates an automatic threshold raising algorithm, which can effectively reduce repetition rates, improve recognition accuracy and efficiency. The recognition program implemented through Python language can quickly and accurately identify welds, providing important technical support for production, manufacturing, and maintenance, and ensuring the safe operation of the factory. The promotion and application of this algorithm can improve production efficiency and quality, reduce product failure rates, and have important practical significance.