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Special Issue "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: 30 April 2024 | Viewed by 6309

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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

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (7 papers)

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Research

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

Jieqiang Zhu

Jiaqi Bao

and

Yi Tao

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

Viewed by 242

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

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 352

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

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

Viewed by 905

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

and

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

Viewed by 841

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

and

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

Cited by 2 | Viewed by 868

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

and

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

Cited by 2 | Viewed by 1594

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)

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 992

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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