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Applied Sciences
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Application of Artificial Intelligence and Computer Vision for Detection and...
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Application of Artificial Intelligence and Computer Vision for Detection and Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 7632

Special Issue Editors

Dr. Hongquan Jiang

E-Mail Website
Guest Editor
State Key Laboratory for Manufacturing System Engineering, Department of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: intelligent manufacturing; AI technology in quality control; computer vision
Dr. Denglong Ma

E-Mail Website
Guest Editor
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: artificial neural networks; inverse methods; computational fluid dynamics; numerical optimization; engineering, applied and computational mathematics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nondestructive testing, represented by X-ray, ultrasonic, thermography, etc., and machine vision testing, represented by photoelectric testing, are important technologies for defect detection and quality control in the manufacturing and service process of complex products, such as aerospace, petrochemical, special equipment, etc. With the rapid improvement in the automation and informatization of current testing instruments, industry has accumulated a large amount of testing data, including waveform, image, video, etc. However, data analysis is mainly conducted manually, which is inefficient and inconsistent, and makes it difficult to meet the requirements of modern quality control. In recent years, artificial intelligence and computer vision technology have developed rapidly, providing an excellent opportunity to develop intelligent detection data analysis technology and application systems. Therefore, in this special issue, recent efforts and advances in artificial intelligence technology and computer vision technology in data analysis and application systems will be discussed.

Dr. Hongquan Jiang
Dr. Denglong Ma
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. Applied Sciences 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 2400 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

  • image preprocessing technology
  • video data preprocessing technology
  • high resolution technology for image or video data
  • defect target detection technology
  • defect target segmentation technology
  • defect target type identification technology
  • intelligent defect rating technology
  • intelligent data analysis software system and its applications

Published Papers (6 papers)

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Research

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13 pages, 2439 KiB  
Article
Robust Airport Surface Object Detection Based on Graph Neural Network
by Wenyi Tang and Hongjue Li
Appl. Sci. 2024, 14(9), 3555; https://doi.org/10.3390/app14093555 - 23 Apr 2024
Viewed by 208
Abstract
Accurate and robust object detection is of critical importance in airport surface surveillance to ensure the security of air transportation systems. Owing to the constraints imposed by a relatively fixed receptive field, existing airport surface detection methods have not yet achieved substantial advancements [...] Read more.
Accurate and robust object detection is of critical importance in airport surface surveillance to ensure the security of air transportation systems. Owing to the constraints imposed by a relatively fixed receptive field, existing airport surface detection methods have not yet achieved substantial advancements in accuracy. Furthermore, these methods are vulnerable to adversarial attacks with carefully crafted adversarial inputs. To address these challenges, we propose the Vision GNN-Edge (ViGE) block, an enhanced block derived from the Vision GNN (ViG). ViGE introduces the receptive field in pixel space and represents the spatial relation between pixels directly. Moreover, we implement an adversarial training strategy with augmented training samples generated by adversarial perturbation. Empirical evaluations on the public remote sensing dataset LEVIR and a manually collected airport surface dataset show that: 1. our proposed method surpasses the original model in precision and robustness; 2. defining the receptive field in pixel space performs better than that on representation space. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Computer Vision for Detection and Analysis)
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24 pages, 41135 KiB  
Article
MSIE-Net: Associative Entity-Based Multi-Stage Network for Structured Information Extraction from Reports
by Qiuyue Li, Hao Sheng, Mingxue Sheng and Honglin Wan
Appl. Sci. 2024, 14(4), 1668; https://doi.org/10.3390/app14041668 - 19 Feb 2024
Viewed by 602
Abstract
Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination [...] Read more.
Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item’s location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net’s capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Computer Vision for Detection and Analysis)
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27 pages, 7905 KiB  
Article
An Advanced Fitness Function Optimization Algorithm for Anomaly Intrusion Detection Using Feature Selection
by Sung-Sam Hong, Eun-joo Lee and Hwayoung Kim
Appl. Sci. 2023, 13(8), 4958; https://doi.org/10.3390/app13084958 - 14 Apr 2023
Cited by 2 | Viewed by 1566
Abstract
Cyber-security systems collect information from multiple security sensors to detect network intrusions and their models. As attacks become more complex and security systems diversify, the data used by intrusion-detection systems becomes more dimensional and large-scale. Intrusion detection based on intelligent anomaly detection detects [...] Read more.
Cyber-security systems collect information from multiple security sensors to detect network intrusions and their models. As attacks become more complex and security systems diversify, the data used by intrusion-detection systems becomes more dimensional and large-scale. Intrusion detection based on intelligent anomaly detection detects attacks based on machine-learning classification models, soft computing, and rule sets. Feature-selection methods are used for efficient intrusion detection and solving high-dimensional problems. Optimized feature selection can maximize the detection model performance; thus, a fitness function design is required. We proposed an optimization algorithm-based feature-selection algorithm to improve anomaly-detection performance. We used a genetic algorithm and proposed an advanced fitness function that finds the most relevant feature set, increasing the detection rate, reducing the error rate, and enhancing analysis speed. An improved fitness function for the selection of optimized features is proposed; this function can address overfitting by solving the problem of anomaly-detection performance from imbalanced security datasets. The proposed algorithm outperformed other feature-selection algorithms. It outperformed the PCA and wrapper-DR methods, with 0.99564 at 10%, 0.996455 at 15%, and 0.996679 at 20%. It performed higher than wrapper-DR by 0.95% and PCA by 3.76%, showing higher differences in performance than in detection rates. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Computer Vision for Detection and Analysis)
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14 pages, 5694 KiB  
Article
Weld Cross-Section Profile Fitting and Geometric Dimension Measurement Method Based on Machine Vision
by Weilong He, Aihua Zhang and Ping Wang
Appl. Sci. 2023, 13(7), 4455; https://doi.org/10.3390/app13074455 - 31 Mar 2023
Cited by 2 | Viewed by 1445
Abstract
A visual measurement method based on a key point detection network is proposed for the difficulty of fitting the cross-sectional profile of ultra-narrow gap welds and the low efficiency and accuracy of manual measurement of geometric parameters. First, the HRnet (High-Resolution Net) key [...] Read more.
A visual measurement method based on a key point detection network is proposed for the difficulty of fitting the cross-sectional profile of ultra-narrow gap welds and the low efficiency and accuracy of manual measurement of geometric parameters. First, the HRnet (High-Resolution Net) key point detection algorithm was used to train the feature point detection model, and 18 profile feature points in a “measurement unit” were extracted. Secondly, the feature point coordinates are transformed from the image coordinate system to the weld coordinate system, and the weld profiles are fitted by the least squares method. Finally, the measurement system is calibrated with a coplanar linear calibration algorithm to perform pixel distance to actual distance conversion for quantitative detection of geometric dimensions. The experimental results show that the accuracy of the proposed method for key point localization is 95.6%, the mean value of the coefficient of determination R-square for curve fitting is greater than 94%, the absolute error of measurement is between 0.06 and 0.15 mm, and the relative error is between 1.27% and 3.12%. The measurement results are more reliable, and the efficiency is significantly improved compared to manual measurement. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Computer Vision for Detection and Analysis)
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Review

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20 pages, 925 KiB  
Review
Advancing OCR Accuracy in Image-to-LaTeX Conversion—A Critical and Creative Exploration
by Everistus Zeluwa Orji, Ali Haydar, İbrahim Erşan and Othmar Othmar Mwambe
Appl. Sci. 2023, 13(22), 12503; https://doi.org/10.3390/app132212503 - 20 Nov 2023
Viewed by 1458
Abstract
This paper comprehensively assesses the application of active learning strategies to enhance natural language processing-based optical character recognition (OCR) models for image-to-LaTeX conversion. It addresses the existing limitations of OCR models and proposes innovative practices to strengthen their accuracy. Key components of this [...] Read more.
This paper comprehensively assesses the application of active learning strategies to enhance natural language processing-based optical character recognition (OCR) models for image-to-LaTeX conversion. It addresses the existing limitations of OCR models and proposes innovative practices to strengthen their accuracy. Key components of this study include the augmentation of training data with LaTeX syntax constraints, the integration of active learning strategies, and the employment of active learning feedback loops. This paper first examines the current weaknesses of OCR models with a particular focus on symbol recognition, complex equation handling, and noise moderation. These limitations serve as a framework against which the subsequent research methodologies are assessed. Augmenting the training data with LaTeX syntax constraints is a crucial strategy for improving model precision. Incorporating symbol relationships, wherein contextual information is considered during recognition, further enriches the error correction. This paper critically examines the application of active learning strategies. The active learning feedback loop leads to progressive improvements in accuracy. This article underlines the importance of uncertainty and diversity sampling in sample selection, ensuring that the dynamic learning process remains efficient and effective. Appropriate evaluation metrics and ensemble techniques are used to improve the operational learning effectiveness of the OCR model. These techniques allow the model to adapt and perform more effectively in diverse application domains, further extending its utility. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Computer Vision for Detection and Analysis)
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14 pages, 675 KiB  
Review
Progress of Machine Vision Technologies in Intelligent Dairy Farming
by Yongan Zhang, Qian Zhang, Lina Zhang, Jia Li, Meian Li, Yanqiu Liu and Yanyu Shi
Appl. Sci. 2023, 13(12), 7052; https://doi.org/10.3390/app13127052 - 12 Jun 2023
Cited by 2 | Viewed by 1418
Abstract
The large-scale and precise intelligent breeding mode for dairy cows is the main direction for the development of the dairy industry. Machine vision has become an important technological means for the intelligent breeding of dairy cows due to its non-invasive, low-cost, and multi-behavior [...] Read more.
The large-scale and precise intelligent breeding mode for dairy cows is the main direction for the development of the dairy industry. Machine vision has become an important technological means for the intelligent breeding of dairy cows due to its non-invasive, low-cost, and multi-behavior recognition capabilities. This review summarizes the recent application of machine vision technology, machine learning, and deep learning in the main behavior recognition of dairy cows. The authors summarized identity recognition technology based on facial features, muzzle prints, and body features of dairy cows; motion behavior recognition technology such as lying, standing, walking, drinking, eating, rumination, estrus; and the recognition of common diseases such as lameness and mastitis. Based on current research results, machine vision technology will become one of the important technological means for the intelligent breeding of dairy cows. Finally, the author also summarized the advantages of this technology in intelligent dairy farming, as well as the problems and challenges faced in the next development. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Computer Vision for Detection and Analysis)
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