带钢表面缺陷在线检测系统的设计

Design of Online Surface Defect Inspection System for Steel Strips

针对带钢表面缺陷人工检测方法效率低下的问题,设计了一套在线自动检测系统.首先,提出了带钢表面缺陷在线检测系统的总体设计方案,包括系统的硬件结构、软件结构以及图像采集系统.随后,着重研究了在线检测系统中所涉及的图像预处理方法、图像分割方法、特征提取选择和缺陷分类方法.通过缺陷区域频率域图像特征的提取和缺陷的人工神经网络分类,提高了分类结果的准确性.最后,采用常见缺陷的样本对该系统进行测试,实验结果验证了算法的有效性.

An online surface defect detection low efficiency in traditional manual methods system for steel strips was designed to solve the problem of Firstly, the overall design scheme of the system, including the hardware structure, software structure and image acquisition system, was proposed. Then, the preprocessing and segmentation methods of images, the extraction and selection of features and the defect classification methods were studied. With the help of features extracted from the frequency domain image of defect region and defect classification based on artificial neural networks, the accuracy of the defect classification was improved. Finally, the system was tested by the samples of common defects and the experiment results verified the effectiveness of the proposed algorithms.