基于级联卷积神经网络的复杂花色布匹瑕疵检测算法

Defect Detection Algorithm of Complex Pattern Fabric Based on Cascaded Convolution Neural Network

当布匹的背景信息复杂多变时,复杂花色布匹的瑕疵定位与分类较为困难.针对这一问题,文中提出基于级联卷积神经网络的复杂花色布匹瑕疵检测算法.首先,使用双路残差的骨干特征提取网络,在缺陷图和模板图上提取并融合特征.然后,设计密度聚类边框生产器,指导框架中区域候选网络的预检测框设计.最后,通过级联回归方法完成瑕疵的精确定位和分类.采用工业现场采集的布匹图像数据进行训练与预测,结果表明,文中算法的精准率和召回率较高.

In defect location and classification of complex colored fabric,it is difficult to locate and classify defects in the cloth with complex and changeable background information.To solve this problem,a defect detection algorithm of complex pattern fabric based on cascaded convolution neural network is proposed.Firstly,the backbone feature extraction network based on two-way residual is applied to extract and fuse features from defect map and template map.Then,a density clustering frame producer is designed to guide the design of pre inspection frame for regional candidate networks in the framework.Finally,the cascaded regression method is utilized to locate and classify the defects accurately.The cloth image data collected from industrial field is adopted for training and prediction.The final results show that the proposed algorithm achieves high accuracy and recall rate.