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A computer vision system for Pallets Verification in Quality Control

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2023年第7期24卷 1221-1234页

作者： de Morais, Marcus Vinicius Barbosa dos Santos, Sara Dereste Pires, Ricardo Fed Inst Educ Sci & Technol Sao Paulo Elect Dept Rua Pedro Vicente 625 BR-01109010 Sao Paulo SP Brazil

Flexible manufacturing systems have gained their place in industries thanks to the possibility of being adapted to a particular task. A robotized palletizing cell is an example of such a system, which is responsible for organizing and stacking loads to be transported. A key part in this process is the pallet and if it presents a problem, the whole cell may stop. Furthermore, it is possible that manipulator robots, transported loads, and any other part of the cell suffer damage. To avoid these problems, the objective of this work is to develop an artificial vision system capable of selecting wood pallets that are in good condition, which means free of defects such as splinters, cracks and broken or out of dimensions elements. The artificial vision system has been developed based on image processing and machine learning techniques and presented better results compared to the past studies. Image processing parameters of Canny and Hough Transform have been adjusted, followed by comparisons among Support-Vector Machines with different kernels and their parameters. An accuracy of 100% in the classification has been obtained based on the Support-Vector Machine with radial basis function kernel.

关键词： computer vision system Pallets Quality control Support-vector machine Image processing

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Evaluation of low sodium Kačkavalj cheese properties using 3D scanning, scanning electron microscopy and computer vision system

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INTERNATIONAL JOURNAL OF DAIRY TECHNOLOGY 2024年第2期77卷 586-593页

作者： Satric, Ana Tomasevic, Igor Djekic, Ilija Pavlovic, Vladimir Levic, Steva Miocinovic, Jelena Univ Belgrade Fac Agr Dept Anim Source Technol Nemanjina 6 Belgrade 11080 Serbia DIL German Inst Food Technol D-49610 Quakenbrueck Germany Univ Belgrade Fac Agr Dept Food Safety & Qual Management Nemanjina 6 Belgrade 11080 Serbia Univ Belgrade Fac Agr Dept Math & Phys Nemanjina 6 Belgrade 11080 Serbia Univ Belgrade Fac Agr Dept Food Technol & Biochem Nemanjina 6 Belgrade 11080 Serbia

Salt reduction in food is one of the main concerns for public health agencies. Performing two-way analysis of variance, the effects of salt reduction and replacement on the pasta filata Ka & ccaron;kavalj cheese properties were studied during 6 months. computer vision system is a novel method for colour evaluation. There were no differences in colour parameters L*, a* and b* (90.40-92.87, 0.07-0.60 and 11.40-18.53 respectively) and microstructure analysed by scanning electron microscopy between variants during ripening. Three-dimensional scanning revealed that low sodium variants showed lower volume reduction. Salt reduction in Ka & ccaron;kavalj cheese results in high quality product and contributes to consumer health.

关键词： Ka & ccaron kavalj cheese 3D scanning Microstructure computer vision system Reduced sodium

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Color assessment of the eggs using computer vision system and Minolta colorimeter

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JOURNAL OF FOOD MEASUREMENT AND CHARACTERIZATION 2021年第6期15卷 5097-5112页

作者： Milovanovic, Bojana Tomovic, Vladimir Djekic, Ilija Solowiej, Bartosz G. Lorenzo, Jose M. Barba, Francisco J. Tomasevic, Igor Univ Belgrade Fac Agr Nemanjina 6 Belgrade 11080 Serbia Univ Novi Sad Fac Technol Bul Cara Lazara 1 Novi Sad 21000 Serbia Univ Life Sci Lublin Fac Food Sci & Biotechnol Dept Milk Technol & Hydrocolloids Skromna 8 PL-20704 Lublin Poland Fdn Ctr Tecnolox Carne San Cibrao Das Vinas Ourense Spain Univ Vigo Fac Ciencias Ourense Area Tecnol Alimentos Orense 32004 Spain Univ Valencia Fac Pharm Food Sci Toxicol & Forens Med Dept Nutr & Food Sci AreaPrevent Med & Publ Hlth Avda Vicent Andres Estelles S-N Valencia 46100 Spain

The scientific journal articles (n = 150) were examined to obtain instrumental egg color data published in the period 2009-2020. The majority of articles originated from Asia (42.0%), investigated yolk color (45.3%), selected Minolta device (65.3%). The greatest part of papers failed to include parameters such as port size (92.0%), observer (90.0%), illuminant (84.0%), technical replicates (70.0%) and calibration method (66.7%), and, therefore, this represents a key matter in conducting comparative research to ensure equivalence in order to trace and compare different research data. Furthermore, the usage of a computer vision system (CVS) for egg color analysis was investigated. The color of five egg species (chicken, goose, duck, quail and turkey) was estimated using a CVS and a traditional colorimeter. The CVS-produced color was highly similar to the actual color of egg sample (ranging from 75.0 to 100.0%). The color of eggshell gathered through the Minolta depicts brighter, less "red" and more "yellow" appearance than CVS. Regarding the color of yolk samples, Minolta had lighter (except the goose's yolk), more "green" and less "yellow" color, whereas CVS indicated the appearance of albumen as lighter (except quail's), more "red" and less "yellow" than colorimeter. Those results read by Minolta showed a non-real color of egg samples, whereas CVS-obtained color was highly similar to the actual egg color sample. Considering these results, it could be concluded that the CVS is a superior alternative for replacing traditional devices by providing better accuracy.

关键词： Color measurement Egg color computer vision system Colorimeter Visual similarity tests

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Using latent class modelling to evaluate the performance of a computer vision system for pig carcass contamination

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PREVENTIVE VETERINARY MEDICINE 2025年 241卷 106556页

作者： Lund, Daniel Hjorth Alban, Lis Hansen, Claus Dalsgaard, Anders Denwood, Matt Olsen, Abbey Danish Technol Inst Sect Anim Welf Taastrup Denmark Univ Copenhagen Dept Vet & Anim Sci Frederiksberg Denmark Danish Agr & Food Council Copenhagen Denmark

This study evaluates the performance of a computer vision system (CVS) for measuring pig carcass contamination using latent class modelling, a statistical approach that does not depend on a gold standard. Developed by the Danish Technological Institute, the CVS integrates output from various cameras to inspect pig carcasses for presence of faecal contamination. Data from a 16-day period involving 69,215 carcasses were analysed, comparing CVS results with those from official auxiliaries. Descriptive analyses identified four meat inspection findings that were statistically associated with an increased relative risk of positives from the CVS, particularly oil contamination (RR = 4.1, P < 0.001), which the CVS could not differentiate from faecal contamination. Agreement between the CVS and official auxiliary was assessed using Cohen's kappa and prevalence- and bias-adjusted kappa (PABAK), with Cohen's Kappa indicating minimal agreement (kappa = 0.17) and PABAK indicating moderate agreement (kappa = 0.79). Sensitivity and specificity were estimated using a latent class model fit within a Bayesian framework, without assuming that either the CVS or official auxiliaries were perfect tests. The latent class model showed that the CVS had a median sensitivity of 31.6 % (95 % CI: 27.6 %-39.1 %) and specificity of 97.9 % (95 % CI: 96.1-99.9 %), compared to 22 % (95 % CI: 17.6 %-28.9 %) sensitivity and 99 % (95 % CI: 98.2 %-100 %) specificity for the official auxiliaries. These findings underscore the CVS's strength in detecting true contaminations and official auxiliaries' ability to rule out non-contaminations. This study demonstrates the applicability of latent class modelling for evaluating CVS, offering a flexible and reliable framework that addresses the limitations of traditional gold standard methods. The findings support the use CVS technology alongside traditional inspections to enhance food safety, paving the way for future integration of CVS in meat inspection, pendin

关键词： Latent class modelling Machine vision Meat safety assurance Carcass evaluation computer vision system

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A computer vision system and machine learning algorithms for prediction of physicochemical changes and classification of coated sweet cherry

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Heliyon 2024年第20期10卷 e39484页

作者： Shahedi, Yashar Zandi, Mohsen Bimakr, Mandana Department of Food Science and Engineering Faculty of Agriculture University of Zanjan Zanjan 45371-38791 Iran

The current research utilized visual characteristics obtained from RGB images and qualitative characteristics to investigate changes in surface defects, predict physical and chemical characteristics, and classify sweet cherries during storage. It was achieved with the help of ANN (Artificial Neural Network) and ANFIS (Adaptive Neuro-Fuzzy Inference system) models. The ANN used in this study was a Multilayer Perceptron (MLP) with SigmoidAxon and TanhAxon threshold functions, trained with the Momentum training function. Additionally, ANFIS with a Mamdani system and Triangle, Gauss, and Trapezoidal membership functions, was employed to predict sweet cherries' physical and chemical properties and their quality classification. Both models incorporate four algorithms. Additionally, the algorithms use color statistical features and color texture features combined with physical and chemical properties, including weight loss, firmness, titratable acidity, and total anthocyanin content. The image color and texture characteristics were used by ANN and ANFIS models to predict physical and chemical properties with high accuracy. ANN and ANFIS models accurately estimate sweet cherry quality grades in all four algorithms with over 90 % accuracy. According to the findings, the ANN and ANFIS models have demonstrated satisfactory performance in the qualitative classification and prediction of sweet cherries' physical and chemical properties. © 2024 The Authors

关键词： Adaptive neuro-fuzzy inference system Artificial neural network Classification computer vision system Sweet cherry

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A computer vision system for objective fabric smoothness appearance assessment with an ensemble classifier

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TEXTILE RESEARCH JOURNAL 2020年第3-4期90卷 333-343页

作者： Wang, Jingan Shi, Kangjun Wang, Lei Pan, Ruru Gao, Weidong Jiangnan Univ Key Lab Ecotext Wuxi Jiangsu Peoples R China

Fabric smoothness appearance assessment plays an important role in the textile and apparel industry. To evaluate fabric smoothness objectively, different methods have been proposed based on computer vision technology. To further improve the performance and promote the application of the assessment methods, this paper reports a hybrid computer vision system for objective assessment of fabric smoothness appearance with an ensemble classifier to integrate the advantages of the different image feature sets, which are extracted based on different image processing technologies. The image acquisition environment is established in this system with the selection of illumination parameters-intensity, position angle and altitudinal angle-by a designed strategy. The main steps of the strategy include determination of priority by information gain analysis and parameter selection by classifier performance analysis. The support vector machine classifiers trained by each feature sets are grouped into an ensemble by a self-adapting weighted voting method and the redundant feature sets are eliminated based on the weights of the feature sets. The final result shows evaluation accuracies with 82.86% under 0-degree error, 97.14% under 0.5-degree error and 100% under 1-degree error, which outperforms the other methods in the same environment and verifies the applicability of the proposed system.

关键词： Fabric smoothness textile testing ensemble classifier computer vision system

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computer vision system for froth-middlings interface level detection in the primary separation vessels

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computerS & CHEMICAL ENGINEERING 2019年 123卷 357-370页

作者： Vicente, Agustin Raveendran, Rahul Huang, Biao Sedgh, Shabnam Narang, Anuj Jiang, Hailei Mitchell, Warren Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada Spartan Controls Edmonton AB T6E 5L9 Canada

Bitumen from the surface-mined oil sands is separated through a water-based gravity separation process in the Primary Separation Vessels (PSV). The froth-middlings interface level in the PSVs is the most important control variable in the process. Bitumen recovery and downstream operations are critically dependent on the interface level measurement and control. In this paper, we describe a robust computer vision system that uses image and data processing techniques to estimate the froth-middlings interface level. The algorithm in the computer vision system processes the online video frames of a camera mounted to the PSV sight glasses, and it is based on a combination of static and dynamic image processing steps. Industrial results show that the computer vision algorithm is more accurate and reliable when compared to the other instruments, as well as robust against process and environmental abnormalities. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： computer vision system Image processing Oil sands extraction Primary separation vessels Froth-middlings interface level

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Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics

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MICROCHEMICAL JOURNAL 2021年第PartA期160卷 105600-105600页

作者： Ren, Guangxin Gan, Ning Song, Yan Ning, Jingming Zhang, Zhengzhu Anhui Agr Univ State Key Lab Tea Plant Biol & Utilizat Hefei 230036 Anhui Peoples R China Anhui Agr Univ Sch Engn Hefei 230036 Anhui Peoples R China

The feature of external shape in tea is a vital quality index that determines the rank quality of tea. The potential of a lab-made computer vision system (CVS) coupled with morphological features and chemometric tools is investigated for evaluating Congou black tea quality. First, Raw images of 700 tea samples from seven different quality grades are acquired using the CVS. The original images collected are processed by graying, binarization, and median de-noising. Then, six morphological parameters (viz. width, length, area, perimeter, length-width ratio, and rectangularity) from the samples are extracted by the shape segmentation of each tea leaf image, and the corresponding feature histogram is obtained. Finally, support vector machine (SVM) and least squares support vector machine (LS-SVM) are utilized to build identification models based on the histogram distribution characteristic vectors. Three kernel methods (linear kernel, polynomial kernel, and radial basis function kernel) are compared for monitoring tea quality. The results show that the optimal LS-SVM model has a 12% higher correct discrimination rate (CDR) than the SVM model. The polynomial kernel LS-SVM model yields satisfactory classification results with the CDR of 100% based on selected six shape features in the calibration and prediction sets. This work demonstrates that it is feasible to discriminate Congou black tea quality using CVS technology along with morphological features and nonlinear chemometric methods. A new perspective on the sizes of morphological characteristics is proposed as an identifier of Congou black tea quality.

关键词： Congou black tea computer vision system Morphological features Least squares-support vector machine Kernel method

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On-line weight estimation of broiler carcass and cuts by a computer vision system

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POULTRY SCIENCE 2021年第12期100卷 101474页

作者： Nyalala, Innocent Okinda, Cedric Makange, Nelson Korohou, Tchalla Qi Chao Nyalala, Luke Zhang Jiayu Zuo Yi Yousaf, Khurram Liu Chao Chen Kunjie Nanjing Agr Univ Coll Engn Nanjing 210031 Jiangsu Peoples R China Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

In a broiler carcass production conveyor system, inspection, monitoring, and grading carcass and cuts based on computer vision techniques are challenging due to cuts segmentation and ambient light conditions issues. This study presents a depth image-based broiler carcass weight prediction system. An Active Shape Model was developed to segment the carcass into 4 cuts (drumsticks, breasts, wings, and head and neck). Five regression models were developed based on the image features for each weight estimation (carcass and its cuts). The Bayesian-ANN model outperformed all other regression models at 0.9981 R-2 and 0.9847 R-2 in the whole carcass and head and neck weight estimation. The RBF-SVR model surpassed all the other drumstick, breast, and wings weight prediction models at 0.9129 R-2, 0.9352 R-2, and 0.9896 R-2, respectively. This proposed technique can be applied as a nondestructive, nonintrusive, and accurate on-line broiler carcass production system in the automation of chicken carcass and cuts weight estimation.

关键词： broiler carcasses carcass weight computer vision system regression modeling statistical modeling

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Non-destructive and contactless quality evaluation of table grapes by a computer vision system

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computerS AND ELECTRONICS IN AGRICULTURE 2019年 156卷 558-564页

作者： Cavallo, Dario Pietro Cefola, Maria Pace, Bernardo Logrieco, Antonio Francesco Attolico, Giovanni CNR Natl Res Council Italy Inst Intelligent Syst Automat Via G Amendola 122-O I-70126 Bari Italy CNR Natl Res Council Italy Inst Sci Food Prod Via G Amendola 122-O I-70126 Bari Italy CNR Natl Res Council Italy Inst Sci Food Prod URT CS DAT Via Michele Protano I-71121 Foggia Italy CNR Natl Res Council Italy Inst Intelligent Ind Syst & Technol Adv Mfg Via G Amendola 122-O I-70126 Bari Italy

Quality rating is currently accomplished by non-destructive and subjective sensory evaluation or by objective and destructive analytical techniques. There is a strong need of an objective non-destructive contactless quality evaluation system to monitor fruit and vegetable along the whole supply chain. This paper proposes a computer vision system to satisfy this request. Image processing and machine learning techniques have been combined to develop a computer vision system whose configuration and tuning has been strongly simplified: that makes easier its deployment in real applications. The system has been verified on two white table grape cultivars (Italia and Victoria) against three different classification tasks. The first considered five quality levels (5, 4, 3, 2, 1);the second separated the higher fully marketable quality levels (5 and 4) from the boundary (3) and the waste (2 and 1);the third separated the higher fully marketable quality levels (5 and 4) from the other three (3, 2 and 1). The system achieved a cross-validation classification accuracy up to 92% on the cultivar Victoria and up to 100% on the cultivar Italia for binary or binomial classification between fully marketable and residual quality levels. The obtained results support its capability of powerfully, flexibly and continuously monitoring the quality of the complete production along the whole supply chain.

关键词： Table grapes Quality evaluation computer vision system Random forest classifier

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