Leather 皮革

[2022] An improved automatic defect identification system on natural leather via generative adversarial network

Herein, an automatic inspection system is introduced to distinguish whether a leather patch contains any defective areas. In particular, the dataset involved in this experiment comprises a total of 375 images that are made up of three classes, viz, black line defect, wrinkle defect and non-defective. Notably, owing to the data scarcity issue, the Generative Adversarial Network (GAN) is adopted to discover the feature regularities to produce plausible additional training samples. The proposed algorithm is systematically analysed, and compelling performance is yielded when compared to previous works. Succinctly, with a relatively small amount of readily captured training data, the classification performance is capable of achieving 100% accuracy.

[2022] Automated Classification System for Tick-Bite Defect on Leather

This paper introduces an efficient automated defect classification framework that is capable to evaluate if the sample patches contain defective segments. A six-step preprocessing procedure is introduced to enhance the quality of the leather image in terms of visibility and to preserve important features representation. .en, multiple classifiers are utilized to differentiate between defective and nondefective leather patches. .e proposed framework is capable to generate a classification accuracy rate of 94% from a collection of samples of 1600 pieces of calf leather patches.

[2021] Detection and localization of defects on natural leather surfaces

This paper proposes an automatic leather defect localization and detection system by employing a series of digital image processing methods based on deep learning. Succinctly, a convolutional neural network (CNN) is utilized to perform the detection task, that is to determine the presence of the defect on a leather patch. Then, the detected defective leather patch is processed to the localization operation, which is to identify the boundary coordinates in pixel level. For the detection task, the result achieved using AlexNet as the feature descriptor and SVM as the classifier is 100%. For the localization stage, we have demonstrated that the instance segmentation technique, Faster R-CNN outperforms the YOLOv2 by obtaining the Intersection over Union (IoU) of 73%. In addition, extensive experiments and comparisons of the state-of-the-art approaches are presented to verify the effectiveness of the proposed algorithms.

[2020] Automated leather defect inspection using statistical approach on image intensity

This paper proposes a method that based on image processing techniques, namely, gray level histogram analysis, to detect defects of the leather. Specifically, the histogram characteristics such as the mean and standard deviation are extracted and treated as the features. Then, the statistical Kolmogorov–Smirnov’s two-sample test is utilized to perform feature selection. Followed by a thresholding method to reduce the dimensionality of the features. Finally, the features are categorized by several well-known classifiers. The best classification accuracy obtained are 99.16% and 77.13% on two different datasets respectively. 

[2020] Leather defect classification and segmentation using deep learning architecture

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%.