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Computer Vision and Sensing Technologies for Industrial Quality Inspection

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 25 July 2024 | Viewed by 11204

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Special Issue Editors

Prof. Dr. Hong-Dar Lin

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Guest Editor

Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: computer vision; optical inspection; quality management; automated industrial inspection
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Cheng-Hsiung Hsieh

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Guest Editor

Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: image processing; computer vision; signal filtering; artificial intelligence; grey system with applications

Prof. Dr. Hsin-Chieh Wu

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Guest Editor

Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan
Interests: ergonomics and design; ambient intelligence; industrial management

Special Issue Information

Dear Colleagues,

Today, although quality inspections play an essential role in a successful operation, finding effective ways to carry out them can be a challenge. Combined with advanced computer vision and sensing technologies, quality inspection can become an essential tool for various intelligent applications in smart manufacturing and production, such as object detection, classification, tracking, and counting. The trend is to reach human-level precision or more in quality inspection with automation. Computer vision-based applications minimize human intervention, optimize operational efficiency, and reduce labor costs. In addition, new sensing technologies have provided us with an excellent ability to measure, inspect, sort, and grade products effectively and efficiently.

This special issue calls for research papers through use cases of artificial intelligence techniques and showcases the need to optimize algorithms, inference frameworks, and hardware accelerators to obtain good performance in quality inspection. It mainly focuses on computer vision and sensing technologies for industrial quality inspection, including, but not limited to, imaging techniques, image processing methods, vision systems, and system optimization. Industrial inspection papers are also welcome, such as quality inspection with machine learning and data-driven methods. Both review articles and original research papers are sought in this special issue.

Prof. Dr. Hong-Dar Lin
Prof. Dr. Cheng-Hsiung Hsieh
Prof. Dr. Hsin-Chieh Wu
Guest Editors

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Keywords

computer vision
sensing technologies
industrial quality inspection
automatic optical inspection
artificial intelligence techniques
machine learning
deep learning

Published Papers (10 papers)

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Research

18 pages, 4698 KiB

Open AccessArticle

A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

by Yuhang Zhu, Zhezhuang Xu, Ye Lin, Dan Chen, Zhijie Ai and Hongchuan Zhang

Sensors 2024, 24(5), 1635; https://doi.org/10.3390/s24051635 - 02 Mar 2024

Viewed by 488

Abstract

Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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15 pages, 6196 KiB

Open AccessArticle

Four-Stage Multi-Physics Simulations to Assist Temperature Sensor Design for Industrial-Scale Coal-Fired Boiler

by Tanuj Gupta, Mahabubur Rahman, Xinyu Jiao, Yongji Wu, Chethan K. Acharya, Dock R. Houston, Susan Maley, Junhang Dong, Hai Xiao and Huijuan Zhao

Sensors 2024, 24(1), 154; https://doi.org/10.3390/s24010154 - 27 Dec 2023

Viewed by 747

Abstract

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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27 pages, 140776 KiB

Open AccessArticle

Practical Applications of a Set-Based Camera Deployment Methodology

by Edward Parrott, Joshua K. Pickard and Rickey Dubay

Sensors 2024, 24(1), 111; https://doi.org/10.3390/s24010111 - 25 Dec 2023

Viewed by 488

Abstract

This work establishes a complete methodology for solving continuous sets of camera deployment solutions for automated machine vision inspection systems in industrial manufacturing facilities. The methods presented herein generate constraints that realistically model cameras and their associated intrinsic parameters and use set-based solving methods to evaluate these constraints over a 3D mesh model of a real part. This results in a complete and certifiable set of all valid camera poses describing all possible inspection poses for a given camera/part pair, as well as how much of the part’s surface is inspectable from any pose in the set. These methods are tested and validated experimentally using real cameras and precise 3D tracking equipment and are shown to accurately align with real imaging results according to the hardware they are modelling for a given inspection deployment. In addition, their ability to generate full inspection solution sets is demonstrated on several realistic geometries using realistic factory settings, and they are shown to generate tangible, deployable inspection solutions, which can be readily integrated into real factory settings. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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13 pages, 2232 KiB

Open AccessArticle

A Nondestructive Methodology for Determining Chemical Composition of Salvia miltiorrhiza via Hyperspectral Imaging Analysis and Squeeze-and-Excitation Residual Networks

by Jieqiang Zhu, Jiaqi Bao and Yi Tao

Sensors 2023, 23(23), 9345; https://doi.org/10.3390/s23239345 - 23 Nov 2023

Viewed by 543

Abstract

The quality assurance of bulk medicinal materials, crucial for botanical drug production, necessitates advanced analytical methods. Conventional techniques, including high-performance liquid chromatography, require extensive pre-processing and rely on extensive solvent use, presenting both environmental and safety concerns. Accordingly, a non-destructive, expedited approach for assessing both the chemical and physical attributes of these materials is imperative for streamlined manufacturing. We introduce an innovative method, designated as Squeeze-and-Excitation Residual Network Combined Hyperspectral Image Analysis (SE-ReHIA), for the swift and non-invasive assessment of the chemical makeup of bulk medicinal substances. In a demonstrative application, hyperspectral imaging in the 389–1020 nm range was employed in 187 batches of Salvia miltiorrhiza. Notable constituents such as salvianolic acid B, dihydrotanshinone I, cryptotanshinone, tanshinone IIA, and moisture were quantified. The SE-ReHIA model, incorporating convolutional layers, maxpooling layers, squeeze-and-excitation residual blocks, and fully connected layers, exhibited

R_{c}^{2}

values of 0.981, 0.980, 0.975, 0.972, and 0.970 for the aforementioned compounds and moisture. Furthermore,

R_{p}^{2}

values were ascertained to be 0.975, 0.943, 0.962, 0.957, and 0.930, respectively, signifying the model’s commendable predictive competence. This study marks the inaugural application of SE-ReHIA for Salvia miltiorrhiza’s chemical profiling, offering a method that is rapid, eco-friendly, and non-invasive. Such advancements can fortify consistency across botanical drug batches, underpinning product reliability. The broader applicability of the SE-ReHIA technique in the quality assurance of bulk medicinal entities is anticipated with optimism. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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21 pages, 4923 KiB

Open AccessArticle

A New Texture Spectrum Based on Parallel Encoded Texture Unit and Its Application on Image Classification: A Potential Prospect for Vision Sensing

by José Trinidad Guillen Bonilla, Nancy Elizabeth Franco Rodríguez, Héctor Guillen Bonilla, Alex Guillen Bonilla, Verónica María Rodríguez Betancourtt, Maricela Jiménez Rodríguez, María Eugenia Sánchez Morales and Oscar Blanco Alonso

Sensors 2023, 23(20), 8368; https://doi.org/10.3390/s23208368 - 10 Oct 2023

Viewed by 689

Abstract

In industrial applications based on texture classification, efficient and fast classifiers are extremely useful for quality control of industrial processes. The classifier of texture images has to satisfy two requirements: It must be efficient and fast. In this work, a texture unit is [...] Read more.

3 \times 3

, a new texture spectrum called Texture Spectrum based on the Parallel Encoded Texture Unit (TS_PETU) is proposed, calculated, and used as a characteristic vector in a multi-class classifier, and then two image databases are classified. The first database contains images from the company Interceramic^®® and the images were acquired under controlled conditions, and the second database contains tree stems and the images were acquired in natural environments. Based on our experimental results, the TS_PETU satisfied both requirements (efficiency and speed), was developed for binary images, and had high efficiency, and its compute time could be reduced by applying parallel coding concepts. The classification efficiency increased by using larger observational windows, and this one was selected based on the window size. Since the TS_PETU had high efficiency for Interceramic^®® tile classification, we consider that the proposed technique has significant industrial applications. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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23 pages, 5665 KiB

Open AccessArticle

Unmanned Aerial Systems and Deep Learning for Safety and Health Activity Monitoring on Construction Sites

by Aliu Akinsemoyin, Ibukun Awolusi, Debaditya Chakraborty, Ahmed Jalil Al-Bayati and Abiola Akanmu

Sensors 2023, 23(15), 6690; https://doi.org/10.3390/s23156690 - 26 Jul 2023

Cited by 3 | Viewed by 1620

Abstract

Construction is a highly hazardous industry typified by several complex features in dynamic work environments that have the possibility of causing harm or ill health to construction workers. The constant monitoring of workers’ unsafe behaviors and work conditions is considered not only a proactive but also an active method of removing safety and health hazards and preventing potential accidents on construction sites. The integration of sensor technologies and artificial intelligence for computer vision can be used to create a robust management strategy and enhance the analysis of safety and health data needed to generate insights and take action to protect workers on construction sites. This study presents the development and validation of a framework that implements the use of unmanned aerial systems (UASs) and deep learning (DL) for the collection and analysis of safety activity metrics for improving construction safety performance. The developed framework was validated using a pilot case study. Digital images of construction safety activities were collected on active construction sites using a UAS, and the performance of two different object detection deep-learning algorithms/models (Faster R-CNN and YOLOv3) for safety hardhat detection were compared. The dataset included 7041 preprocessed and augmented images with a 75/25 training and testing split. From the case study results, Faster R-CNN showed a higher precision of 93.1% than YOLOv3 (89.8%). The findings of this study show the impact and potential benefits of using UASs and DL in computer vision applications for managing safety and health on construction sites. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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19 pages, 7259 KiB

Open AccessArticle

Optical Imaging Deformation Inspection and Quality Level Determination of Multifocal Glasses

by Hong-Dar Lin, Tung-Hsin Lee, Chou-Hsien Lin and Hsin-Chieh Wu

Sensors 2023, 23(9), 4497; https://doi.org/10.3390/s23094497 - 05 May 2023

Viewed by 1167

Abstract

Multifocal glasses are a new type of lens that can fit both nearsighted and farsighted vision on the same lens. This property allows the glass to have various curvatures in distinct regions within the glass during the grinding process. However, when the curvature varies irregularly, the glass is prone to optical deformation during imaging. Most of the previous studies on imaging deformation focus on the deformation correction of optical lenses. Consequently, this research uses an automatic deformation defect detection system for multifocal glasses to replace professional assessors. To quantify the grade of deformation of curved multifocal glasses, we first digitally imaged a pattern of concentric circles through a test glass to generate an imaged image of the glass. Second, we preprocess the image to enhance the clarity of the concentric circles’ appearance. A centroid-radius model is used to represent the form variation properties of every circle in the processed image. Third, the deviation of the centroid radius for detecting deformation defects is found by a slight deviation control scheme, and we gain a difference image indicating the detected deformed regions after comparing it with the norm pattern. Fourth, based on the deformation measure and occurrence location of multifocal glasses, we build fuzzy membership functions and inference regulations to quantify the deformation’s severity. Finally, a mixed model incorporating a network-based fuzzy inference and a genetic algorithm is applied to determine a quality grade for the deformation severity of detected defects. Testing outcomes show that the proposed methods attain a 94% accuracy rate of the quality levels for deformation severity, an 81% recall rate of deformation defects, and an 11% false positive rate for multifocal glass detection. This research contributes solutions to the problems of imaging deformation inspection and provides computer-aided systems for determining quality levels that meet the demands of inspection and quality control. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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21 pages, 23263 KiB

Open AccessArticle

Optical Panel Inspection Using Explicit Band Gaussian Filtering Methods in Discrete Cosine Domain

by Hong-Dar Lin, Huan-Hua Tsai, Chou-Hsien Lin and Hung-Tso Chang

Sensors 2023, 23(3), 1737; https://doi.org/10.3390/s23031737 - 03 Feb 2023

Cited by 2 | Viewed by 1111

Abstract

Capacitive touch panels (CTPs) have the merits of being waterproof, antifouling, scratch resistant, and capable of rapid response, making them more popular in various touch electronic products. However, the CTP has a multilayer structure, and the background is a directional texture. The inspection work is more difficult when the defect area is small and occurs in the textured background. This study focused mainly on the automated defect inspection of CTPs with structural texture on the surface, using the spectral attributes of the discrete cosine transform (DCT) with the proposed three-way double-band Gaussian filtering (3W-DBGF) method. With consideration to the bandwidth and angle of the high-energy region combined with the characteristics of band filtering, threshold filtering, and Gaussian distribution filtering, the frequency values with higher energy are removed, and after reversal to the spatial space, the textured background can be weakened and the defects enhanced. Finally, we use simple statistics to set binarization threshold limits that can accurately separate defects from the background. The detection outcomes showed that the flaw detection rate of the DCT-based 3W-DBGF approach was 94.21%, the false-positive rate of the normal area was 1.97%, and the correct classification rate was 98.04%. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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21 pages, 9493 KiB

Open AccessArticle

Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment

by Lintao Han, Hengyi Lv, Yuchen Zhao, Hailong Liu, Guoling Bi, Zhiyong Yin and Yuqiang Fang

Sensors 2023, 23(1), 427; https://doi.org/10.3390/s23010427 - 30 Dec 2022

Cited by 2 | Viewed by 2128

Abstract

To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking advantage of transformers to capture long-range dependencies, and of local information perception (LIP) to model local features for enhanced representation learning. The module increases the model’s understanding of the image content. Dual path pooling (DPP) is used to keep more contextual image quality information in feature downsampling. Experimental results verify that Conv-Former not only outperforms the state-of-the-art methods on authentic image databases, but also achieves competing performances on synthetic image databases which demonstrate the strong fitting performance and generalization capability of our proposed model. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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11 pages, 3315 KiB

Open AccessArticle

Inline Quality Monitoring of Reverse Extruded Aluminum Parts with Cathodic Dip-Paint Coating (KTL)

by Alexander Pierer, Markus Hauser, Michael Hoffmann, Martin Naumann, Thomas Wiener, Melvin Alexis Lara de León, Mattias Mende, Jiří Koziorek and Martin Dix

Sensors 2022, 22(24), 9646; https://doi.org/10.3390/s22249646 - 09 Dec 2022

Viewed by 1283

Abstract

Perfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts. Full article

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection)

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