基于ViBe的端到端铝带表面缺陷检测识别方法

End-to-end aluminum strip surface defects detection and recognition method based on ViBe

针对铝带表面缺陷高精度检测要求以及传统算法识别率不佳的问题,提出端到端的表面缺陷检测与识别方法.从铝带表面初始图像序列中快速计算出平均图像,视为无缺陷背景图像,用于初始化ViBe算法的背景模型.采用ViBe算法从当前图像中分割出缺陷区域,对缺陷区域二值图像进行中值滤波和形态学运算,以去除噪声点和修补边缘,实现缺陷区域的准确提取.利用当前图像实时更新ViBe背景模型,以增加对光照变化的适应能力.提取缺陷外接矩形区域图像,归一化后输入到训练好的卷积神经网络中进行识别分类,得到分类结果.实验结果表明,提出方法的缺陷检出率为93.02%,缺陷识别率为99.86%,具有较好的应用价值.

An end-to-end surface defects detection and recognition method was proposed to solve the problem of high-precision detection of aluminum strip surface defects and the poor recognition rate of traditional algorithms.The average image was quickly calculated from the initial image sequence of aluminum strip surface,which was regarded as defect-free background image and was used to initialize the background model of the ViBe algorithm.The ViBe algorithm was used to segment the defect region from the current image.Median filtering and morphological operation were performed on the binary image of defect region to remove noise points and repair edges in order to accurately extract the defect region.The current image was used to update the ViBe background model in real time in order to increase the adaptability of the algorithm to illumination changes.The image of external rectangular region of the defect was extracted,normalized,and input into the trained convolutional neural networks for recognition and classification.The classification result was obtained.The experimental results show that the proposed method has a defect detection rate of 93.02%and a defect recognition rate of 99.86%,which has good application value.