焊缝表面缺陷激光视觉传感HOG-SVM的检测方法

为了实现对焊缝表面缺陷的自动检测与分类，研究一种有效识别焊缝表面缺陷的激光视觉检测方法.通过激光视觉传感器采集焊缝图像并进行预处理，包括图像分割，灰度化，平滑去噪以及焊缝轮廓提取.采用方向梯度直方图(Histogram of Oriented Gradient, HOG)提取焊缝激光条纹轮廓图像的特征向量.其次，基于5折-交叉验证网格搜索方法进行模型参数寻优，最终建立了支持向量机(Support Vector Machine, SVM)智能模型识别与分类焊缝表面缺陷.通过调整焊缝轮廓提取算法、HOG特征维度得到不同特征数据并进行对比、分析焊缝缺陷的识别效果.在相同试验条件下，发现支持向量机比随机森林分类器、K最近邻分类器以及朴素贝叶斯分类器的识别率更高，达到97.86%.基于HOG-SVM的焊缝表面缺陷智能识别方法可有效提高焊缝缺陷(气孔、凹陷、咬边)及无缺陷的分类精度.

Study on HOG-SVM detection method of weld surface defects using laser visual sensing

In order to realize automatic detection and classification of weld surface defects, an effective laser vision detection method for weld surface defects was studied. First, the weld image is collected by a laser vision sensor and preprocessed, including image segmentation, grayscale, smooth denoising and weld contour extraction. Then the feature vectors of laser stripe contour image of weld seam are extracted by means of Histogram of Oriented Gradient (HOG), the model parameters were optimized based on the five-fold cross-validation grid search method. Finally, an intelligent model of Support Vector Machine (SVM) was established to identify and classify weld surface defects. Different feature data were obtained by adjusting the weld contour extraction algorithm and HOG feature dimension, and then the identification effect of weld defects was analyzed by comparing each other. Under same experimental conditions, it is found that the recognition rate of SVM is higher than that of random forest classifier, K-nearest neighbor classifier and naive BAYES classifier, reaching 97.86%. The proposed intelligent identification method of weld surface defects based on HOG-SVM can effectively improve the classification accuracy of weld defects (porosity, sag, undercut) and non-defects.