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Reza MN, Ali MR, Samsuzzaman, Kabir MSN, Karim MR, Ahmed S, Kyoung H, Kim G, Chung SO. Thermal imaging and computer vision technologies for the enhancement of pig husbandry: a review. J Anim Sci Technol 2024; 66:31-56. [PMID: 38618025 PMCID: PMC11007457 DOI: 10.5187/jast.2024.e4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 04/16/2024]
Abstract
Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.
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Affiliation(s)
- Md Nasim Reza
- Department of Smart Agricultural Systems,
Graduate School, Chungnam National University, Daejeon 34134,
Korea
- Department of Agricultural Machinery
Engineering, Graduate School, Chungnam National University,
Daejeon 34134, Korea
| | - Md Razob Ali
- Department of Agricultural Machinery
Engineering, Graduate School, Chungnam National University,
Daejeon 34134, Korea
| | - Samsuzzaman
- Department of Agricultural Machinery
Engineering, Graduate School, Chungnam National University,
Daejeon 34134, Korea
| | - Md Shaha Nur Kabir
- Department of Agricultural Industrial
Engineering, Faculty of Engineering, Hajee Mohammad Danesh Science and
Technology University, Dinajpur 5200, Bangladesh
| | - Md Rejaul Karim
- Department of Agricultural Machinery
Engineering, Graduate School, Chungnam National University,
Daejeon 34134, Korea
- Farm Machinery and Post-harvest Processing
Engineering Division, Bangladesh Agricultural Research
Institute, Gazipur 1701, Bangladesh
| | - Shahriar Ahmed
- Department of Agricultural Machinery
Engineering, Graduate School, Chungnam National University,
Daejeon 34134, Korea
| | - Hyunjin Kyoung
- Division of Animal and Dairy Science,
Chungnam National University, Daejeon 34134, Korea
| | - Gookhwan Kim
- National Institute of Agricultural
Sciences, Rural Development Administration, Jeonju 54875,
Korea
| | - Sun-Ok Chung
- Department of Smart Agricultural Systems,
Graduate School, Chungnam National University, Daejeon 34134,
Korea
- Department of Agricultural Machinery
Engineering, Graduate School, Chungnam National University,
Daejeon 34134, Korea
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Liu Y, Wang F, Jiang Z, Sfarra S, Liu K, Yao Y. Generative Deep Learning-Based Thermographic Inspection of Artwork. Sensors (Basel) 2023; 23:6362. [PMID: 37514656 PMCID: PMC10383240 DOI: 10.3390/s23146362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/27/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023]
Abstract
Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.
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Affiliation(s)
- Yi Liu
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fumin Wang
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Zhili Jiang
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Stefano Sfarra
- Department of Industrial and Information Engineering and Economics, University of L'Aquila, Piazzale E. Pontieri n. 1, Monteluco di Roio, I-67100 L'Aquila, Italy
| | - Kaixin Liu
- Shanxi Key Laboratory of Signal Capturing & Processing, North University of China, Taiyuan 030051, China
| | - Yuan Yao
- Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan
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Moradi M, Ghorbani R, Sfarra S, Tax DM, Zarouchas D. A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography. Sensors (Basel) 2022; 22:9361. [PMID: 36502062 PMCID: PMC9740253 DOI: 10.3390/s22239361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/25/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, machine learning and deep learning techniques are effective tools that should be employed properly in accordance with the experiment's nature and the collected data. Considering both the temporal and spatial perspectives of step-heating thermography, a spatiotemporal deep neural network is developed for defect identification in a mock-up reproducing an artwork. The results are then compared with those of other conventional algorithms, demonstrating that the proposed approach outperforms the others.
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Affiliation(s)
- Morteza Moradi
- Structural Integrity & Composites Group, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
- Center of Excellence in Artificial Intelligence for Structures, Prognostics & Health Management, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
| | - Ramin Ghorbani
- Pattern Recognition Laboratory, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
| | - Stefano Sfarra
- Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Piazzale E. Pontieri 1, Monteluco di Roio, 67100 L’Aquila, Italy
| | - David M.J. Tax
- Pattern Recognition Laboratory, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Van Mourik Broekmanweg 6, 2628 XE Delft, The Netherlands
| | - Dimitrios Zarouchas
- Structural Integrity & Composites Group, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
- Center of Excellence in Artificial Intelligence for Structures, Prognostics & Health Management, Aerospace Engineering Faculty, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
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Dritsa V, Orazi N, Yao Y, Paoloni S, Koui M, Sfarra S. Thermographic Imaging in Cultural Heritage: A Short Review. Sensors (Basel) 2022; 22:s22239076. [PMID: 36501785 PMCID: PMC9735770 DOI: 10.3390/s22239076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/16/2022] [Accepted: 11/20/2022] [Indexed: 05/14/2023]
Abstract
Over the recent period, there has been an increasing interest in the use of pulsed infrared thermography (PT) for the non-destructive evaluation of Cultural Heritage (CH). Unlike other techniques that are commonly employed in the same field, PT enables the depth-resolved detection of different kinds of subsurface features, thus providing helpful information for both scholars and restorers. Due to this reason, several research activities are currently underway to further improve the PT effectiveness. In this manuscript, the specific use of PT for the analysis of three different types of CH, namely documentary materials, panel paintings-marquetery, and mosaics, will be reviewed. In the latter case, i.e., mosaics, passive thermography combined with ground penetrating radar (GPR) and digital microscopy (DM) have also been deepened, considering their suitability in the open field. Such items have been selected because they are characterized by quite distinct physical and structural properties and, therefore, different PT (and, in some cases, verification) approaches have been employed for their investigations.
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Affiliation(s)
- Vasiliki Dritsa
- NDT Laboratory, Department of Materials Science & Engineering, School of Chemical Engineering, National Technical University of Athens, Iroon Polytechniou No. 9, Zografou Campus, 15780 Athens, Greece
| | - Noemi Orazi
- Dipartimento di Ingegneria Industriale, Università Degli Studi di Roma Tor Vergata, Via Del Politecnico 1, 00133 Rome, Italy
| | - Yuan Yao
- Department of Chemical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Stefano Paoloni
- Dipartimento di Ingegneria Industriale, Università Degli Studi di Roma Tor Vergata, Via Del Politecnico 1, 00133 Rome, Italy
| | - Maria Koui
- NDT Laboratory, Department of Materials Science & Engineering, School of Chemical Engineering, National Technical University of Athens, Iroon Polytechniou No. 9, Zografou Campus, 15780 Athens, Greece
| | - Stefano Sfarra
- Department of Industrial and Information Engineering and Economics (DIIIE), University of L’Aquila, Monteluco di Roio, 67100 L’Aquila, Italy
- Correspondence: ; Tel.: +39-340-615-1350
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Kim S. Blockchain Smart Contract to Prevent Forgery of Degree Certificates: Artificial Intelligence Consensus Algorithm. Electronics 2022; 11:2112. [DOI: 10.3390/electronics11142112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
Abstract
Certificates are often falsified, such as fake diplomas and forged transcripts. As such, many schools and educational institutions have begun to issue diplomas online. Although diplomas can be issued conveniently anytime, anywhere, there are many cases wherein diplomas are forged through hacking and forgery. This paper deals with the required Blockchain diploma. In addition, we use an automatic translation system, which incorporates natural language processing, to perform verification work that does not require an existing public certificate. The hash algorithm is used to authenticate security. This paper also proposes the use of these security protocols to provide more secure data protection. In addition, each transaction history, whether a diploma is true or not, may be different in length if it is presented in text, but converting it into a hash function means that it is always more than a certain length of SHA-512 or higher. It is then verified using the time stamp values. These chaining codes are designed. This paper also provides the necessary experimental environment. At least 10 nodes are constructed. Blockchain platform development applies and references Blockchain standardization, and a platform test, measurement test, and performance measurement test are conducted to assess the smart contract development and performance measurement. A total of 500 nodes were obtained by averaging 200 times, and a Blockchain-based diploma file was agreed upon at the same time. It shows performance information of about 4100 TPS. In addition, the analysis of artificial intelligence distribution diagram was conducted using a four-point method, and the distribution chart was evenly distributed, confirming the diploma with the highest similarity. The verified values were then analyzed. This paper proposes these natural language processing-based Blockchain algorithms.
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Bu X, Jiang M. Extraction of Intangible Cultural Heritage Visual Elements by Deep Learning and Its Application in Grassland Tourism of the Silk Road Culture. Comput Intell Neurosci 2022; 2022:3242960. [PMID: 35769282 PMCID: PMC9236835 DOI: 10.1155/2022/3242960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/14/2022] [Accepted: 05/27/2022] [Indexed: 11/17/2022]
Abstract
Inner Mongolia is rich in grassland tourism resources, and the development of grassland tourism is of great significance to Inner Mongolia tourism and promotion of grassland protection. To better promote the grassland tourism of the Silk Road culture, the Conditional Global Area Network (CGAN) and Morphology Connected Component Chan-Vase (MCC-CV) algorithm are used to enhance and segment the traditional embroidery patterns in Inner Mongolia. Firstly, the generative adversarial network (GAN) is optimized, and a new GAN is proposed with the feature vector extracted from the convolutional neural network (CNN) as the constraint condition. Secondly, the automatic segmentation algorithm of embroidery based on the MCC-CV model is proposed, and finally, the proposed algorithm is tested. The test results demonstrate that after 8000 iterations of the proposed image-enhancement algorithm, its personalized features are enhanced, and the segmentation accuracy of the proposed image segmentation algorithm is 60%. The proposed algorithm provides some ideas for the application of deep learning (DL) technology in the grassland tourism of the Silk Road culture and also helps operators to accurately grasp the market and make tourists more comfortable and pleasant.
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Affiliation(s)
- Xiangwei Bu
- Academy of Fine Arts, Inner Mongolia Minzu University, Tongliao 028000, China
| | - Mingyang Jiang
- College of Computer Science and Technology, Inner Mongolia Minzu University, Tongliao 028000, China
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Fernandes H, Summa J, Daudre J, Rabe U, Fell J, Sfarra S, Gargiulo G, Herrmann H. Characterization of Ancient Marquetry Using Different Non-Destructive Testing Techniques. Applied Sciences 2021; 11:7979. [DOI: 10.3390/app11177979] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Non-destructive testing of objects and structures is a valuable tool, especially in cultural heritage where the preservation of the inspected sample is of vital importance. In this paper, a decorative marquetry sample is inspected with three non-destructive testing (NDT) techniques: air-coupled ultrasound, X-ray micro-tomography, and infrared thermography. Results from the three techniques were compared and discussed. X-ray micro-tomography presented the most detailed results. On the other hand, infrared thermography provided interesting results with the advantage of being cheap and easy in the deployment of the NDT method.
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