ABSTRACT The defects on a leather surface may be caused by the poor material handling process during the production and manufacturing stages. It is essential to eliminate the natural variations and artificial injuries on the leather surfaces, in order to control the quality of the products and achieve customer satisfaction. To date, the visual inspection of the leather defects is performed manually by human operators. Thus, this paper aims to introduce an automatic defect detection technique by employing a deep learning method. Specifically, the proposed method consists of two stages: classification and instance segmentation. The former stage distinguishes whether the piece of the leather sample contains a defective part or not, whereas the latter is to localize the precise defective location. To accomplish the tasks, the dataset is first collected under a proper laboratory environment. Among 250 defective samples and 125 non-defective samples, the proposed method has been demonstrated its feature learning capability by producing promising performance when considering relatively fewer training samples. Particularly, the defect types focused in this study are the black lines and wrinkles. The best performance obtained is ∼95% for the classification task, whereas the segmentation task reaches an Intersection over Union rate of 99.84%

中文翻译：

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使用深度学习架构的皮革缺陷分类和分割

摘要皮革表面的缺陷可能是由于生产和制造阶段的材料处理过程不当造成的。必须消除皮革表面的自然变化和人为损伤，以控制产品质量并实现客户满意度。迄今为止，皮革缺陷的目视检查是由人工操作员手动执行的。因此，本文旨在通过采用深度学习方法引入自动缺陷检测技术。具体来说，所提出的方法包括两个阶段：分类和实例分割。前一阶段区分皮革样品是否包含有缺陷的部分，而后者是定位精确的缺陷位置。为了完成任务，数据集首先在适当的实验室环境下收集。在 250 个有缺陷的样本和 125 个无缺陷的样本中，所提出的方法通过在考虑相对较少的训练样本时产生有希望的性能来证明其特征学习能力。特别是，本研究中关注的缺陷类型是黑线和皱纹。分类任务获得的最佳性能约为 95%，而分割任务的交集率达到 99.84% 本研究中关注的缺陷类型是黑线和皱纹。分类任务获得的最佳性能约为 95%，而分割任务的交集率达到 99.84% 本研究重点关注的缺陷类型是黑线和皱纹。分类任务获得的最佳性能约为 95%，而分割任务的交集率达到 99.84%