With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting model, to recognize the type of the defect in the fabric. In brief, the proposed framework mainly consists of two steps, namely local defect prediction and global defect recognition. Experiments on the dataset have demonstrated the superior performance in fabric defect detection.

中文翻译：

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基于深度卷积神经网络的织物缺陷检测采用两阶段策略

随着劳动力成本的上升和纺织行业自动化程度的提高，织物疵点检测成为近年来的一个热门研究领域。我们提出了一种基于学习的织物缺陷自动检测框架。首先，我们使用固定大小的方形滑块将原始图像裁剪为一定的步长和规律。然后使用改进的直方图均衡化来增强每个裁剪的图像。此外，采用 Inception-V1 模型来预测局部区域是否存在缺陷。最后，我们应用 LeNet-5 模型，它扮演投票模型的角色，来识别织物中的缺陷类型。简而言之，所提出的框架主要包括两个步骤，即局部缺陷预测和全局缺陷识别。