A Multi-Level Colour Thresholding Based Segmentation Approach for Improved Identification of the Defective Region in Leather Surfaces

Vision systems are recently adopted for defect detection in leather surface to overcome difficulties of labour intensive, time consuming manual inspection process. Suitable image processing techniques needs to be developed for accurate detection of leather defects. Existing research works have focused for gray scale based image processing techniques which requires conversion of colour images using an averaging method and it lacks sensitivity for detecting the leather defects due to the random and texture surface of the leather.  This work presents a colour processing approach for improved identification of leather defects using a multi-level thresholding function. In this work, the colour leather images are processed in ‘Lab’ colour domain for improving the human perception of discriminating the leather defects.  In the present work, the specific range of values for the colour attributes of different leather defect in colour leather samples are identified using the colour histogram.  MATLAB software routine is developed for identifying defects in specific ranges of colour attributes and the results are presented.  From the results, it is found that proposed provides a simpler approach for identifying the defective regions based on the colour attributes of the surface with improved human perception. The proposed methodology can be implemented in graphical processing units for efficiently detecting several types of defects using specific thresholds for the automated real-time inspection of leather defects

An Extended Review on Fabric Defects and Its Detection Techniques

In Textile Industry, Quality of the Fabric is the main important factor. At the initial stage, it is very essential to identify and avoid the fabrics faults/defects and hence human perception consumes lot of time and cost to reveal the fabrics faults. Now-a-days Automated Inspection Systems are very useful to decrease the fault prediction time and gives best visualizing clarity- based on computer vision and image processing techniques. This paper made an extended review about the quality parameters in the fiber-to-fabric process, fabrics defects detection terminologies applied on major three clusters of fabric defects knitting, woven and sewing fabric defects. And this paper also explains about the statistical performance measures which are used to analyze the defect detection process. Also, comparison among the methods proposed in the field of fabric defect detection

Fabric Defect Detection with Deep Learning and False Negative Reduction

Quality control is an area of utmost importance for fabric production companies. By not detecting the defects present in the fabrics, companies are at risk of losing money and reputation with a damaged product. In a traditional system, an inspection accuracy of 60-75% is observed. In order to reduce these costs, a fast and automatic defect detection system, which can be complemented with the operator decision, is proposed in this paper. To perform the task of defect detection, a custom Convolutional Neural Network (CNN) was used in this work. To obtain a well-generalized system, in the training process, more than 50 defect types were used. Additionally, as an undetected defect (False Negative - FN) usually has a higher cost to the company than a non-defective fabric being classified as a defective one (false positive), FN reduction methods were used in the proposed system. In testing, when the system was in automatic mode, an average accuracy of 75% was attained; however, if the FN reduction method was applied, with intervention of the operator, an average of 95% accuracy can be achieved. These results demonstrate the ability of the system to detect many different types of defects with good accuracy whilst being faster and computationally simple.publishersversionpublishe

Deep Learning Strategies for Industrial Surface Defect Detection Systems

Deep learning methods have proven to outperform traditional computer vision methods in various areas of image processing. However, the application of deep learning in industrial surface defect detection systems is challenging due to the insufficient amount of training data, the expensive data generation process, the small size, and the rare occurrence of surface defects. From literature and a polymer products manufacturing use case, we identify design requirements which reflect the aforementioned challenges. Addressing these, we conceptualize design principles and features informed by deep learning research. Finally, we instantiate and evaluate the gained design knowledge in the form of actionable guidelines and strategies based on an industrial surface defect detection use case. This article, therefore, contributes to academia as well as practice by (1) systematically identifying challenges for the industrial application of deep learning-based surface defect detection, (2) strategies to overcome these, and (3) an experimental case study assessing the strategies' applicability and usefulness

FABLE : Fabric Anomaly Detection Automation Process

Unsupervised anomaly in industry has been a concerning topic and a stepping stone for high performance industrial automation process. The vast majority of industry-oriented methods focus on learning from good samples to detect anomaly notwithstanding some specific industrial scenario requiring even less specific training and therefore a generalization for anomaly detection. The obvious use case is the fabric anomaly detection, where we have to deal with a really wide range of colors and types of textile and a stoppage of the production line for training could not be considered. In this paper, we propose an automation process for industrial fabric texture defect detection with a specificity-learning process during the domain-generalized anomaly detection. Combining the ability to generalize and the learning process offer a fast and precise anomaly detection and segmentation. The main contributions of this paper are the following: A domain-generalization texture anomaly detection method achieving the state-of-the-art performances, a fast specific training on good samples extracted by the proposed method, a self-evaluation method based on custom defect creation and an automatic detection of already seen fabric to prevent re-training.Comment: 7th International Conference on Control, Automation and Diagnosis (ICCAD'23), 6 page

A comparative study of texture analysis algorithms in textile inspection applications

Nowadays, quality control is an important problem for fabric manufacturers. Typically these operations have been carried out by humans operators. However, this method has numerous drawbacks such as low precision, performance and effectiveness. Therefore, automatic inspection systems have increased substantially in the last decade. This work evaluates the performance of some texture measures in textile defect detection applications. For classification a method based on leaving-one-out is used. Our study has been carried out using a large database of samples to take into account a wide spectrum of fabrics and multiple defects of different nature reported by specialized works and publications. A ranking with the effectiveness of best algorithms is presented for every type of fabric. In addition, the computation time of algorithms is compared.This work is partially backed by the European Community (FEDER project)

Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface

In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques

Leather inspection and characterization using non-destructive techniques

Leather is a widely used component of many products such as shoes, car seats, garments and other leather goods. Because it is a natural material, a tanned hide will contain visual and hidden flaws. In addition, its mechanical properties vary over the hide. At present, hides are inspected and assessed by skilled operatives. Further, current objective leather testing requires removal of samples and is either destructive and/or incompatible with real time operation, and little or no information about the rest of the skin is provided.A novel mechanical scanning system was built for non-destructive leather testing. The investigation was focused on two of the most important physical leather properties, static compressibility across thickness and tensile properties for low strain regions. The results of static compression energy measurements for a compressive strain of 10 percent, showed a close agreement with the results of tests performed by a conventional compressibility tester. Further, the results of strain energy and stress measurements for a strain of 2 percent, revealed a very good correlation with the results of conventional tensile tests for a similar strain.The application of infrared thermography, a non destructive and contact less technique, to leather characterisation and inspection was investigated in this work. It was shown that this technique could be used for detecting defects in leather, as well as for estimating their size and deepness. However, defect visibility by infrared thermography is conditioned by the fact that a defective area has to cause different material properties or produce an internal thermal resistance. Further, the prohibitive cost of infrared thermography cameras for automation is a serious limitation for its application in current leather testing. It is recommended that the ideal testing system would be based on the combination of mechanical scanning, normal computer vision and infrared thermography. The normal computer vision part of this system would be responsible for measuring area and detecting defects that are visible in nature. The infrared thermography part of the system would be responsible for detecting the type of defects overlooked by the previous method, as well as some thermo-physical parameters. Finally, the Mechanical Scanning System would provide the physical properties of leather, like compressibility, tensile modulus, shear stress and softness that the vision based inspection systems are incapable of providing. In this way, every single skin could be completely characterised in terms of defects and physical properties

Optimized Fuzzy C-means Clustering Methods for Defect Detection on Leather Surface

833-836In this paper, captured images are segmented for the defective part, that is used for the further process of grading the quality of the products using automated inspection systems employed in industries such as leather, fabrics, textiles, tiles... etc.. These industries are the greatest conventional industries that need automatic detection systems as a basic part in diminishing investigation time and expanding production rate. Initially in this work, the input image is wet blue leather fed into a contrast enhancement process that improves the visibility of the image features. This contrast-enhanced image is employed with segmentation process that utilizes Fuzzy C-means algorithm (FCM) technique. This paper proposes two different optimization techniques, Grey Wolf Optimization (GWO) & Monarch Butterfly Optimization (MBO) for executing centroid optimization in FCM and results are compared with Modified Region Growing with GWO of leather segmentation method. The results exemplify that incorporation of optimization technique with FCM has a quite evident impact on segmentation accuracy of 96.90% over context techniques