Abstract
Detection of surface defects on wet-blue leather is much more challenging than raw-hide leather. Since wet-blue leather turns blue and contains moisture after pre-treatment, it is a semi-product of the cowhide processing. At present, the defect detection of wet-blue leather is mostly carried out manually and is time-consuming and labor-intensive for professional inspectors. This paper is the first to use hyperspectral imaging (HSI) to implement the surface inspection of five wet-blue leather defects including brand masks, rotten grain, rupture, insect bites, and scratches in the pixel level detection. Hyperspectral Leather Defect Detection Algorithm (HLDDA) including Hyperspectral Target Detection (HTD) and Deep Learning (DL) techniques was proposed in this paper. In HTD, Weighted Background Suppression Constrained Energy Minimization (WBS-CEM) and WBS-Hierarchical CEM (WBS-hCEM) were developed in this paper by using weighting to suppress the background and enhance the contrast between the target and background. Experimental results showed that the overall performance of WBS was better than the original CEM. In the DL part, 1D-Convolutional Neural Network (CNN), 2D-Unet and 3D-UNet architectures were designed to segment defect areas. For various characteristics of defects, 1D-CNN emphasizes on defects with spectral features, 2D-Unet emphasizes on defects with spatial features, and 3D-Unet can simultaneously process spatial and spectral information in HSI. The experimental results verified that the proposed HLDDA could effectively quantify and estimate the size of the defect, thereby accelerating the leather inspection process by professional inspectors and develop an automated leather grading system towards Industry 4.0.
Highlights
The leather industry is one of the important traditional industries
This study proposed a novel method of using hyperspectral imaging (HSI) to replace the traditional manual wet blue leather inspection mode, known as the Hyperspectral Leather Defect Detection Algorithm (HLDDA)
The experimental results show that the 3D-UNet has the best performance in detecting rupture and rotten grain, and scratches defects, whereas the 1D-Convolutional Neural Network (CNN) has better performance in detecting insect bites and 2D-Unet has the best result in detecting brand masks
Summary
The leather industry is one of the important traditional industries. The produced leather is mainly supplied for downstream leather goods factories, which use leather as the raw material to make various leather goods, including leather shoes, bags, suitcases, gloves, belts, and sofas. This study proposed a novel method of using HSI to replace the traditional manual wet blue leather inspection mode, known as the Hyperspectral Leather Defect Detection Algorithm (HLDDA). It combines Hyperspectral Target Detection (HTD) and Deep Learning (DL) techniques to locate and quantize the defective area of leather. As the HSI is a 3D image, the spectral and spatial information can be processed simultaneously by 3D-UNet. The experimental results show that the 3D-UNet has the best performance in detecting rupture and rotten grain, and scratches defects, whereas the 1D-CNN has better performance in detecting insect bites and 2D-Unet has the best result in detecting brand masks. This technique will play an important role in the future development of leather grading towards Industry 4.0
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