Nowadays, Deep Convolutional Neural Networks (DCNNs) are widely used in fabric defect detection, which come with the cost of expensive training and complex model parameters. With the observation that most fabrics are defect free in practice, a two-step Cascaded Zoom-In Network (CZI-Net) is proposed for patterned fabric defect detection. In the CZI-Net, the Aggregated HOG (A-HOG) and SIFT features are used to instead of simple convolution filters for feature extraction. Moreover, in order to extract more distinctive features, the feature representation layer and full connection layer are included in the CZI-Net. In practice, Most defect-free fabrics only involve in the first step of our method and avoid a costive computation in the second step, which makes very fast fabric detection. More importantly, we propose the Locality-constrained Reconstruction Error (LCRE) in the first step and Restrictive Locality-constrained Coding (RLC), Bag-of-Indexes (BoI) methods in the second step. We also analyse the connections between different coding methods and conclude that the index of visual words plays an essential role in the coding methods. In conclusion, experiments based on real-world datasets are implemented and demonstrate that our proposed method is not only computationally simple but also with high detection accuracy.
翻译:目前,深革命神经网络(DDCNNS)被广泛用于组织缺陷检测,这需要花费昂贵的培训和复杂的模型参数。考虑到大多数结构在实际操作中是无缺陷的观察,建议采用两步封锁的Zoom-In网络(CZI-Net)进行结构缺陷检测。在CZI-Net中,集成的HOG(A-HOG)和SIFT(SIFT)功能被使用来代替简单的集成过滤器进行特征提取。此外,为了提取更显著的特征,功能代表层和完整连接层被包括在CZI-Net中。在实践中,大多数无缺陷的织物只涉及我们方法的第一步,避免在第二步中进行成本性计算,从而非常迅速地检测结构缺陷。更重要的是,我们提出了第一阶段受地方限制的重建错误(LCRE ) 和 受限制的本地化编码(RLC), Bag-Inexes(BoI) 方法在第二步中被选取的。我们还分析了不同加密的检测方法之间的连接。我们所建的代码方法,只是用到一个基于真实的精确的计算方法,我们所建的图像的索引的计算方法。