With the rapid development of visual inspection technology, computer technology, and image processing technology, machine vision technology has become more and more mature, and the role of quality inspection and control in the steel industry is becoming more and more obvious and important. Defects on the surface of the strip are a key factor affecting the quality inspection process. Its inspection plays an extremely important role in improving the final quality. For a long time, traditional manual inspection methods cannot meet actual production needs, so in-depth research on steel surface defect inspection systems has become the consensus of today’s steel companies. The accuracy and low performance of traditional detection methods can no longer meet the needs of people and society. The surface defect detection method based on machine vision has the characteristics of high accuracy, fast processing speed, and intelligent processing, which is the main trend of surface defect detection. We select a steel plate; take the invariant moment features of the cracks, holes, scratches, oil stains, and other images on it; extract the data results; and analyze them. Then, we read the texture features of these defect images again, extract the data results, and analyze them. The experimental results prove that after the mean value filter and Gaussian filter process the image, the mean variance value MSE is relatively large (), and as the concentration of salt and pepper noise increases, the rate of increase of MSE increases obviously, and as the peak signal-to-noise ratio and the mean variance value MSE increase continuously (), the image distortion is more serious. The method designed in this paper is extremely effective. Improving the surface quality of steel is of great significance to improving market competitiveness.

中文翻译：

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基于人工智能机器视觉的钢板表面缺陷检测系统关键技术

随着视觉检查技术，计算机技术和图像处理技术的飞速发展，机器视觉技术变得越来越成熟，质量检查和控制在钢铁行业中的作用越来越明显和重要。带材表面的缺陷是影响质量检查过程的关键因素。它的检查在提高最终质量中起着极其重要的作用。长期以来，传统的手工检查方法无法满足实际的生产需求，因此对钢表面缺陷检查系统的深入研究已成为当今钢铁公司的共识。传统检测方法的准确性和低性能已无法满足人们和社会的需求。基于机器视觉的表面缺陷检测方法具有精度高，处理速度快，处理智能化的特点，这是表面缺陷检测的主要趋势。我们选择一块钢板；采取裂缝，孔洞，划痕，油渍和其他图像的不变矩特征；提取数据结果；并分析它们。然后，我们再次读取这些缺陷图像的纹理特征，提取数据结果并进行分析。实验结果证明，经过均值滤波和高斯滤波对图像进行处理后，均方差值MSE相对较大（孔，划痕，油渍和其他图像；提取数据结果；并分析它们。然后，我们再次读取这些缺陷图像的纹理特征，提取数据结果并进行分析。实验结果证明，经过均值滤波和高斯滤波对图像进行处理后，均方差值MSE相对较大（孔，划痕，油渍和其他图像；提取数据结果；并分析它们。然后，我们再次读取这些缺陷图像的纹理特征，提取数据结果并进行分析。实验结果证明，经过均值滤波和高斯滤波对图像进行处理后，均方差值MSE相对较大（），并且随着盐和胡椒噪声浓度的增加，MSE的增加速率也明显增加，并且随着峰值信噪比和平均方差值MSE不断增加（），图像失真会更加严重。本文设计的方法非常有效。改善钢的表面质量对提高市场竞争力具有重要意义。