Research On Image Processing Of Robotic Welds Based On Image Enhancement And Edge Detection

With the rapid development of international trade,the use of ships is also increasing,and the demand for anchor chains for ships has also ushered in a boom in development.At present,the production of industrial anchor chains all uses worker welding can no longer meet the supply and demand balance of the industry,and long-term welding work will bring irreversible damage to the health of workers.Therefore,factories and enterprises are gradually using industrial welding robots to replace manual welding work.Industrial welding robots are usually equipped with vision sensors at the end of the robot arm to identify the anchor chain weld seam and prevent welding failure due to workpiece position error during welding operations.In machine vision systems,the resolution of industrial cameras has a direct impact on welding work.Low image resolution and the absence of high-frequency information at the edges of the image can cause errors in the welding procedure,which ultimately leads to welding failure.However,the use of high-resolution cameras is costly,which can increase the burden on factories and enterprises.In order to reduce the production costs of enterprises and factories,and at the same time achieve the same recognition accuracy as images taken by high-resolution cameras.This paper proposes an image processing algorithm based on machine vision,which improves the resolution and contrast of weld images in software at the industrial computer end,and extracts the edge information of weld images with high precision.The research content of this thesis consists of the following three parts:Firstly,due to the poor quality of weld images obtained by industrial cameras,this paper studies the welding image enhancement algorithm,and proposes an improved CLAHE(Contrast Limited Adaptive Histogram Equalization)algorithm to improve the weld image quality.In order to make the resolution of the weld image reach the subsequent operable level,this paper follows the Image Interpolation Algorithm Based on Regional Gradient Estimation(GEI),which makes the clarity of the interpolated weld image increase significantly,which is conducive to the subsequent image enhancement algorithm.The interpolated weld image is enhanced using the improved CLAHE algorithm proposed in this paper.The algorithm first uses improved bilateral filtering to remove the noise of the image and retain more edge detail information.Then,the CLAHE algorithm and Gaussian mask are used to perform differential calculations to improve the contrast of the weld image and strengthen the weld edge information again.By comparing with other enhanced algorithms,the peak signal-to-noise ratio of this paper is improved by about 3.45%～11.37%,and the structural similarity is increased by about 3.64%.The effectiveness of the algorithm for the enhancement of weld images is proved by experiments.Secondly,due to the low accuracy of the existing traditional edge detection algorithms for edge extraction,the subsequent welding trajectory will be offset,which will affect the final accuracy of the workpiece.In this paper,an improved Canny operator edge detection algorithm is proposed.The algorithm first uses the Mallat wavelet transform to decompose the enhanced weld image into high-frequency image and low-frequency image,and uses the improved Canny operator edge detection algorithm for the high-frequency subimage.In view of some shortcomings of the traditional Canny operator,this paper makes the following improvements: the improved bilateral filter is used instead of the Gaussian filter;In the gradient direction of 0 degrees and 90 degrees of the original Sobel operator,an additional gradient direction of 45 degrees and 135 degrees is added;In the original non-maximum suppression,the gradient amplitude of the two subpixel points in the gradient direction in the neighborhood of the obtained center point and the gradient amplitude of the center point are compared.The best threshold is obtained adaptively using the improved OTSU algorithm.Next,the traditional Canny operator edge detection algorithm is used for the decomposed low-frequency sub-images.Finally,the wavelet inverse transform is used to obtain the final edge detection image.By comparing with other traditional edge detection algorithms,the feasibility of this paper is proved.Finally,to prove that the algorithm proposed in this paper can still obtain better results than the existing image processing algorithms in practice.The weld image is preprocessed at the industrial computer end,and the above two algorithms are used in the weld edge detection.At the same time,a control experiment was set up,except for the above two parts using different algorithms,other links were set up consistently.This verifies that the two algorithms proposed in this paper can still achieve high fidelity in practice,improve image quality,and extract edges from image edge information with high precision.