The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.

Surface Defect Detection Methods for Industrial Products: A Review

We propose a learning-based approach for automatic detection of fabric defects Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT) The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics In addition to being compact and fast to compute, these signatures enable accurate localization of defects Our defect detection system is based on three main steps In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics Finally, defects are detected during image inspection by testing local patches using the learned BC Our approach can deal with multiple types of textile fabrics, from simple to more complex ones Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods Note to Practitioners —Fabric defect detection is central to automated visual inspection and quality control in textile manufacturing This paper deals with this problem through a learning-based approach By opposite to several existing approaches for fabric defect detection, which are effective in only some types of fabrics and/or defects, our method can deal with almost all types of patterned fabric and defects To enable both detection and localization of defects, a fabric image is first divided into local blocks, which are representative of the repetitive pattern structure of the fabric Then, statistical signatures are calculated by modeling the distribution of coefficients of an RCT using the finite MoGG The discrimination between defect-free and defective fabrics is then achieved through supervised classification of RCT-MoGG signatures based on expert-labeled examples of defective fabric images Experiments have shown that our method yields very good performance in terms of defect detection and localization In addition to its accuracy, inspection of images can be performed in a fully automatic fashion, whereas only labeled examples are initially required Finally, our method can be easily adapted to a real-time scenario since defect detection on inspected images is performed at the block level, which can be easily parallelized through hardware implementation

Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

Headspace analysis of highly humid samples remains a challenge for artificial olfaction. Based on surface plasmon resonance imaging and bio-based sensors, the NeOse Pro olfactive analyzer yields multivariate data and enhances the statistical discrimination capacity of odor patterns. However, the presence of a high background signal, such as water vapor from aqueous samples, may deteriorate its discriminant ability. Recently, miniaturized pre-concentrators packed with hydrophobic adsorbent have been developed to improve the detection limit of gas analysis methods and to enhance their selectivity by reducing the water’s background signal. This work presents, for the first time, the coupling of a miniaturized silicon micro pre-concentration unit (µPC) to a bio-based opto-electronic nose (NeOse Pro). The results showed that the coupling of a silicon µPC with the NeOse Pro led to an improvement in the detection limit of n-nonane by at least a factor of 125. Additionally, principal component analysis (PCA) of eight different flavored waters showed an enhanced discrimination ability of the coupled set-up in highly humid conditions.

https://www.mdpi.com/2227-9040/8/3/60/pdf

Opto-Electronic Nose Coupled to a Silicon Micro Pre-Concentrator Device for Selective Sensing of Flavored Waters

This study proposes an ensemble differential evolution online sequential extreme learning machine (DE-OSELM) for textile image illumination correction based on the rotation forest framework. The DE...

A novel hybrid model using the rotation forest-based differential evolution online sequential extreme learning machine for illumination correction of dyed fabrics:

An ultra-simple microchannel-free fiber-optic gas-pressure sensor based on a section of quartz capillary is proposed. The resonance peaks and interference fringes can be both shown in the reflection spectrum and they are both sensitive to the variation of the gas refractive index and temperature. The gas-pressure can be demodulated by using sensing matrix. As the quartz capillary is open completely at its end, the pressure response time is reduced to 0.02s for the pressure variation of 0.1MPa. The proposed sensor can have actual applications in 24-hour gas-pressure monitoring and early warning in harsh environment, because of its ultra-simple and all-silica structure, ultra-fast response, good repeatability, and ultra-low fabrication cost.

An Ultra-Simple Microchannel-Free Fiber-Optic Gas-Pressure Sensor With Ultra-Fast Response

Patterned fabrics may be regarded as periodic textures, which are defined as the regular tessellation of a primitive unit. A patterned fabric is considered as defective when a primitive unit is dif...

Defect detection on patterned fabrics using texture periodicity and the coordinated clusters representation

In this work, a dual refractive index and temperature sensor based on an interferometric system and on the empirical mode decomposition (EMD) algorithm is presented. Here, it is shown that the EMD provides a comprehensive way to analyze and decompose complex reflection spectra produced by an interferometric filter build at the tip of an optical fiber. By applying the EMD algorithm, the spectrum can be decomposed into a set of intrinsic mode functions (IMF) from which the temperature and the refractive index can be easily extracted. Moreover, the proposed methodology provides a detailed insight of the behavior of this type of interferometric sensors and allows widening of the dynamic measurement ranges of both variables. Here, for proof of principle purposes, a filter based on a stack of three layers (two of them were thermo-sensitive) was fabricated. Finally, it is shown that the proposed methodology can decompose the experimental measured spectra and to determine the refractive index and the temperature, supporting the mathematical model.

https://www.mdpi.com/1424-8220/20/3/664/pdf

Optical Fiber FP Sensor for Simultaneous Measurement of Refractive Index and Temperature Based on the Empirical Mode Decomposition Algorithm.

Recently, biodigesters have attracted much attention as an efficient alternative for energy generation and organic waste treatment. The final performance of a biodigester depends heavily on the quality of its building process and the selection of its raw material: the geomembrane. The geomembrane is the coat that covers the biodigester used to control the migration of fluids. Therefore, the selection of the proper geomembrane, in terms of thickness, resistance, flexibility, etc., is fundamental. Unfortunately, there are no studies for the selection of geomembranes, and usually, it is an empirical process performed by workers based on their own experience. Such empirical selection might be inaccurate, limited, inconvenient, and even dangerous. In order to assist workers during the building process of a biodigester, this study proposes the use of an Artificial Neural Network (ANN) to classify a geomembrane as appropriate or not appropriate for the manufacture of a biodigester. The ANN is trained with a database built from qualitative and quantitative evaluations of different characteristics of geomembranes. The results indicate that the proposed ANN classifies the most suitable geomembranes with a 99.9% success rate. The proposed ANN becomes a reliable tool that contributes to the quality and safety of a biodigester.

Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

In this work, a novel tailored algorithm to enhance the overall sensitivity of gas concentration sensors based on the Direct Absorption Tunable Laser Absorption Spectroscopy (DA-ATLAS) method is presented. By using this algorithm, the sensor sensitivity can be custom-designed to be quasi constant over a much larger dynamic range compared with that obtained by typical methods based on a single statistics feature of the sensor signal output (peak amplitude, area under the curve, mean or RMS). Additionally, it is shown that with our algorithm, an optimal function can be tailored to get a quasi linear relationship between the concentration and some specific statistics features over a wider dynamic range. In order to test the viability of our algorithm, a basic C 2 H 2 sensor based on DA-ATLAS was implemented, and its experimental measurements support the simulated results provided by our algorithm.

Tailored Algorithm for Sensitivity Enhancement of Gas Concentration Sensors Based on Tunable Laser Absorption Spectroscopy.

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Defect detection on patterned fabrics using texture periodicity and the coordinated clusters representation

Optical Fiber FP Sensor for Simultaneous Measurement of Refractive Index and Temperature Based on the Empirical Mode Decomposition Algorithm.

Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network

Tailored Algorithm for Sensitivity Enhancement of Gas Concentration Sensors Based on Tunable Laser Absorption Spectroscopy.