Automated visual inspection of fabric image using deep learning approach for defect detection

Воронин, В. В.; Sizyakin, Roman; Zhdanova, Marina; Semenishchev, Evgeny A.; Bezuglov, D. A.; Zelemskii, Aleksander A.

doi:10.1117/12.2592872

Cited by 16 publications

(14 citation statements)

References 26 publications

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“…The general scheme of the proposed method is shown in Figure 4. For image enhancement we combined local and global transform based on multi-scale block-rooting processing [1,2].…”

Section: The Defect Detection Methodsmentioning

confidence: 99%

Defect detection and removal for depth map quality enhancement in manufacturing with deep learning

Gapon

Voronin

Sizyakin

et al. 2023

Dimensional Optical Metrology and Inspection for Practical Applications XII

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A system for determination the distance from the robot to the scene is useful for object tracking, and 3-D reconstruction may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by incorrect sections on the depth map because of incorrect distance determination of objects. We present a two-stage approach combining defect detection and depth reconstruction algorithms. The first step is image defects detection based on convolutional auto-encoder (U-Net) and deep feature fusion network (DFFN-Net). The second step is a depth map reconstruction with the exemplar-based and the anisotropic gradient concepts. The proposed modified block fusion algorithm uses a local image descriptor obtained by an automatic encoder for image reconstruction, which extracts image features and depth maps using a decoding network. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on RGB-D benchmark for evaluating manufacturing vision systems.

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“…The general scheme of the proposed method is shown in Figure 4. For image enhancement we combined local and global transform based on multi-scale block-rooting processing [1,2].…”

Section: The Defect Detection Methodsmentioning

confidence: 99%

Defect detection and removal for depth map quality enhancement in manufacturing with deep learning

Gapon

Voronin

Sizyakin

et al. 2023

Dimensional Optical Metrology and Inspection for Practical Applications XII

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“…Traditional algorithms are based on the design of previously known functions based on spectral, structural, statistical, model approaches [4]. Some methods can be based on the use of Gabor filters [5], wavelet transforms [6], Fourier transforms [7], texture descriptors [8], etc.…”

Section: Related Workmentioning

confidence: 99%

“…Inspection of fabric materials by a person has low efficiency, individual differences, and it takes time to train a specialist. Experimental studies conducted in [3] showed that a person can recognize only 50-70% of all fabric defects [4]. In most cases, the inspection of fabric materials is carried out already at the final stage of production.…”

Section: Introductionmentioning

confidence: 99%

Fabric image inspection using deep learning approach

Sizyakin¹,

Voronin²,

Gapon³

et al. 2022

Optical Metrology and Inspection for Industrial Applications IX

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This article presents a two-stage approach, combining novel and traditional algorithms, to image segmentation and defect detection. The first stage is a new method for segmenting fabric images is based on Hamiltonian quaternions and the associative algebra and the active contour model with anisotropic gradient. To solve the problem of loss of important information about color, saturation, and other important information associated color, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. In the second stage, our crack and damage detection method are based on a convolutional autoencoder (U-Net) and deep feature fusion network (DFFN-Net). This solution allows localizing defects with higher accuracy compared to traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset.

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“…Comparisons with the other works found in the scientific literature (e.g. [6], [7], [8], [9], [10], and [11]) cannot be directly made, inasmuch as this module focuses in the detection of defects, rather than…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁mentioning

confidence: 99%

“…Accuracies between 99.16% and 77.13% were obtained. Another work that combines traditional and novel algorithms was proposed in [11], more specifically, digital image processing for visual data enhancement and neural networks for fabric defects detection. In most of the works found in literature, validations are carried out with datasets of fabric/leather defects, such as TILDA [12] and MVTec AD [13].…”

Section: Introductionmentioning

confidence: 99%

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

Adão

Gonzalez

Castilla

et al. 2022

J. Phys.: Conf. Ser.

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In textile/leather manufacturing environments, as in many other industrial contexts, quality inspection is an essential activity that is commonly performed by human operators. Error, fatigue, ergonomic issues, and related costs associated to this fashion of carrying out fabric validation are aspects concerning companies’ strategists, whose mission includes to watch over the physical integrity of their employees, while aiming at enhanced quality control methods implementation towards profit maximization. Considering these challenges from a technical/scientific perspective, machine/deep learning approaches have been showing great skills in adapting a wide range of contexts and, in particular, industrial environments, complementing traditional computer vision methods with characteristics such as increased accuracy while dealing with image classification and segmentation problems, capacity for continuous learning from experts input and feedback, flexibility to easily scale training for new contextualization classes – unknown types of occurrences relevant to characterize a given problem –, among other advantages. The goal of crossing deep learning strategies with fabric inspection processes is pursued in this paper. After providing a brief but representative characterization of the targeted industrial context, in which, typically, fabric rolls of raw-material mats must be processed at a relatively low latency, an Automatic Optical Inspection (AOI) system architecture designed for such environments is revisited [1], for contextualization purposes. Afterwards, a set of deep learning-oriented training methods/processes is proposed in combination with neural networks built based on Xception architecture, towards the implementation of one of the components that integrate the aforementioned system, from which is expected the identification of presence/absence of defective textile/leather raw material at a low-latency. Several models powered by Xception were trained with different tunning parameters, resorting to datasets variations that were set up from raw images of leather, following different annotation strategies (meticulous and rough). The model that performed better reached 96% of accuracy.

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Automated visual inspection of fabric image using deep learning approach for defect detection

Cited by 16 publications

References 26 publications

Defect detection and removal for depth map quality enhancement in manufacturing with deep learning

Defect detection and removal for depth map quality enhancement in manufacturing with deep learning

Fabric image inspection using deep learning approach

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

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