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Rapid and non-destructive quality grade assessment of Hanyuan Zanthoxylum bungeanum fruit using a smartphone application integrating computer vision systems and convolutional neural networks

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FOOD CONTROL 2025年 168卷

作者： Bai, Jinrong Mei, Yingjie He, Fang Long, Feiwu Liao, Ying Gao, Hong Huang, Yina Sichuan Univ West China Sch Publ Hlth Chengdu 610041 Peoples R China Sichuan Univ West China Hosp 4 Chengdu 610041 Peoples R China Sichuan Univ Res Ctr Nutr Metab & Food Safety West China Sch Publ Hlth Dept Gen Surg Bariatr & Metab SurgWest China PUMC Chengdu Peoples R China Sichuan Normal Univ Coll Life Sci Chengdu 610101 Peoples R China Sichuan Univ Coll Biomass Sci & Engn Hlth Food Evaluat Res Ctr Chengdu 610065 Peoples R China

The market for extra-grade Hanyuan Zanthoxylum bungeanum fruit (HZB) products is unfortunately rife with adulterated goods, presenting a pressing challenge that necessitates immediate resolution. Therefore, this study developed a smartphone application integrating computer vision systems (CVS) and convolutional neural networks (CNN) for the identification and grade assessment of HZB. A total of 5360 images of individual ZB, including HZB of different grades and other ZB species, were captured by smartphone and used to train, validate, and test the two classification models, VGG16 and Resnet50, where Resnet50 exhibited better performance (accuracy >97%). This model was further employed with the watershed algorithm to segment and classify ZB clusters, specifically identify the extra-grade HZB with an accuracy 97.67%. Additionally, these models were embedded in a smartphone application, and the application's effectiveness was further validated through rigorous defect rate evaluations. Offering a rapid, non-destructive, and user-friendly solution, this smartphonebased approach marks a significant advancement over traditional methods, making it ideal for both professionals and ordinary consumers, especially in resource-limited settings.

关键词： computer vision systems Convolutional neural networks Smartphone real-time testing Hanyuan Zanthoxylum bungeanum fruit Rapid grade assessment Defect rate

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Towards the availability of video communication in artificial intelligence-based computer vision systems utilizing a multi-objective function

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第1期25卷 231-247页

作者： Sharrab, Yousef O. Alsmadi, Izzat Sarhan, Nabil J. Wayne State Univ Deep Learning Lab Detroit MI USA Irbid Natl Univ Dept Artificial Intelligence Irbid Jordan Hashemite Univ Dept Artificial Intelligence Zarqa Jordan Texas A&M Dept Comp & Cyber Secur San Antonio TX 78224 USA Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

Availability is one of the three main goals of information security. This paper contributes to systems' availability by introducing an optimization model for the adaptation (controlling the capturing, coding, and sending features of the video communication system) of live broadcasting of video to limited and varied network bandwidth and/or limited power sources such as wireless and mobile network cases. We first, analyzed the bitrate-accuracy and bitrate-power characteristics of various video transmission techniques for adapting video communication in Artificial Intelligence-based systems. To optimize resources for live video streaming, we analyze various video parameter settings for adapting the stream to available resources. We consider the object detection accuracy, the bandwidth, and power consumption requirement. The results showed that setting SNR and spatial video encoding features (with upscaling the frames at the destination) are the best techniques that maximizing the object detection accuracy while minimizing the bandwidth and the consumed energy requirements. In addition, we analyze the effectiveness of combining SNR and spatial video encoding features with upscaling and find that we can increase the performance of the streaming system by combining these two techniques. We presented a multi-objective function for determining the parameter or parameters' pairing that provides the optimal object detection's accuracy, power consumption, and bit rate. Results are reported based on more than 15,000 experiments utilizing standard datasets for short video segments and a collected dataset of 300 videos from YouTube. We evaluated results based on the detection index, false-positive index, power consumption, and bandwidth requirements metrics. For a single adaptive parameter, the analysis of the experiment's outcome demonstrate that the multi-objective function achieves object detection accuracy as high as the best while drastically reducing bandwidth requirements

关键词： Video communications Optimization computer vision systems Power consumption Rate-energy-accuracy trade-offs Video stream adaptation Artificial intelligence-based systems Availability of video communication Multi-objective function AI-based computer vision systems

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Experimental Analysis of Optimal Bandwidth Allocation in computer vision systems

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2021年第10期31卷 4121-4130页

作者： Davani, Sina G. Sarhan, Nabil J. Wayne State Univ Dept ECE Detroit MI 48202 USA

This paper considers computer vision (CV) systems in which a central monitoring station receives and analyzes the video streams captured and delivered wirelessly by multiple cameras. It addresses how the bandwidth can be allocated to various cameras by presenting a cross-layer solution that optimizes the overall detection or recognition accuracy. In further contrast with prior work, it presents and develops a real CV system and subsequently provides a detailed experimental analysis of cross-layer optimization. Other unique features of the developed solution include employing the popular HTTP streaming approach, utilizing homogeneous cameras as well as heterogeneous ones with varying capabilities and limitations, and including a new algorithm for estimating the effective medium airtime. The results show that the proposed solution significantly improves the CV accuracy.

关键词： Cameras Streaming media Optimization Monitoring Face recognition C plus plus languages Bandwidth Automated video surveillance bandwidth allocation cross-layer optimization computer vision systems effective airtime estimation face recognition many-to-one video streaming WLAN

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Features for Melanoma Lesions Characterization in computer vision systems 9

Features for Melanoma Lesions Characterization in Computer V...

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9th International Conference on Information, Intelligence, systems and Applications (IISA)

作者： Vocaturo, Eugenio Zumpano, Ester Veltri, Pierangelo Univ Calabria Dept Comp Sci Modelling Elect & Syst Engn DIMES Arcavacata Di Rende Italy Magna Graecia Univ Catanzaro Bioinformat Lab Surg & Med Sci Dept DSMC Catanzaro Italy

ISBN: (纸本)9781538681619

computer vision systems are increasingly used for the early detection of skin diseases, such as malignant melanoma. The various proposals of computer vision systems are characterized by some fundamental common phases: image acquisition, pre-processing, segmentation, features extraction and selection and finally classification. In most of the related papers dealing with this topic, many features are extracted in order to feed classifiers from the simplest to the most sophisticated. Features are typically extracted using digital image processing methods (i.e., segmentation, edge detection and color and structure processing), and an open discussion about the meaning of these features and the objective ways of measuring them is ongoing. Therefore, the need to investigate this topic in order to find a guideline to support new researchers on these issues arises. The present work is a not exhaustive review of the most frequently used features in the elaboration of computer vision systems. The shortcomings in some of the existing studies are highlighted and suggestions for future research are provided.

关键词： feature descriptor malignant melanoma medical image analysis computer vision systems

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Application of computer vision systems to the inspection of contact wire wear

Application of computer vision systems to the inspection of ...

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11th International Conference on Urban Transport and the Environment in the 21st Century

作者： Borromeo, S Aparicio, JL Univ Rey Juan Carlos Dept Comp Sci Stat & Telemat Madrid Spain

ISBN: (纸本)184564008X

Measuring systems which automatically detect the degree of wire wear are necessary in order to carry out efficient infrastructure management and maintenance based on contact wire condition. The problem of wear measurement characterized by some important determining factors like rate of sampling and auscultation conditions is studied in detail. This paper describes different developments, based on computer vision, to measure contact wire wear. In general, computer vision in railways contributes towards improved service quality, reduced costs and increased security. This paper concludes with a comparative analysis of different computer vision systems to measure contact wire wear.

关键词： catenary inspection contact wire wear computer vision systems railways

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Convolutional neural networks of the YOLO class in computer vision systems for mobile robotic complexes

Convolutional neural networks of the YOLO class in computer ...

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International Siberian Conference on Control and Communications (SIBCON)

作者： Zoev, Ivan V. Beresnev, Alexey P. Markov, Nikolay G. Tomsk Polytech Univ Dept Informat Technol Tomsk Russia

ISBN: (纸本)9781538651421

An important scientific direction is the development and study- of computer vision systems (CVS) for mobile robotic complexes. Today, developers of CVS are most often using convolutional neural networks (CNN). For increasing the speed detection of objects on images in CVS, there has been a trend of using CNN, which are hardware-implemented on field programmable gate array (FPGAs). This article shows that the perspective for hardware implementation on the FPGA is the tiny-YOLO (_:NN from the YOLO class. For reduce required FPGA computing resources in this (_:NN, was proposed to use Inception-ResNet modules. We was found that with high detection accuracy of objects in images with minimum resources requirements provide by the tiny-YOLO-InceptionResNet2 network architecture. It is obtained from replacing the fifth tiny-YOLO convolutional layer of the tiny-YOLO CNN with two sequential processing Inception-ResNet 'nodules. Also results of the study of the detection accuracy of objects using the CNN for this architecture with the lack of resource-intensive operations: batch normalization and bias from calculations were given. These studies were performed for different formats of representation numbers in the FPGA.

关键词： mobile robotic systems computer vision systems object detection on images convolutional neural networks field-programmable gate array

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Design of computer vision systems for Optimizing the Threat Detection Accuracy

Design of Computer Vision Systems for Optimizing the Threat ...

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作者： Davani, Sina Gholamnejad Wayne State University

学位级别：Ph.D., Doctor of Philosophy

This dissertation considers computer vision (CV) systems in which a central monitoring station receives and analyzes the video streams captured and delivered wirelessly by multiple cameras. It addresses how the bandwidth can be allocated to various cameras by presenting a cross-layer solution that optimizes the overall detection or recognition accuracy. The dissertation presents and develops a real CV system and subsequently provides a detailed experimental analysis of cross-layer optimization. Other unique features of the developed solution include employing the popular HTTP streaming approach, utilizing homogeneous cameras as well as heterogeneous ones with varying capabilities and limitations, and including a new algorithm for estimating the effective medium airtime. The results show that the proposed solution significantly improves the CV accuracy. Additionally, the dissertation features an improved neural network system for object detection. The proposed system considers inherent video characteristics and employs different motion detection and clustering algorithms to focus on the areas of importance in consecutive frames, allowing the system to dynamically and efficiently distribute the detection task among multiple deployments of object detection neural networks. Our experimental results indicate that our proposed method can enhance the mAP (mean average precision), execution time, and required data transmissions to object detection networks. Finally, as recognizing an activity provides significant automation prospects in CV systems, the dissertation presents an efficient activity-detection recurrent neural network that utilizes fast pose/limbs estimation approaches. By combining object detection with pose estimation, the domain of activity detection is shifted from a volume of RGB (Red, Green, and Blue) pixel values to a time-series of relatively small one-dimensional arrays, thereby allowing the activity detection system to take advantage of highly capable

关键词： computer vision systems Threat detection accuracy Bandwidth

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Developing the elements of perceptive-knowledge representation in computer-vision systems

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JOURNAL OF OPTICAL TECHNOLOGY 2010年第11期77卷 721-725页

作者： Rozhkov, A. S. Natl Res Univ Informat Technol Mech & Opt St Petersburg Russia

The basis for creating an expandable system of interaction of a knowledge base with a priori specified image-processing procedures has been developed. Experimental studies of the proposed model on images from a test sample are evidence that it is possible to use this system to create the elements of a perceptive-knowledge representation for image-interpretation systems. (C) 2010 Optical Society of America.

关键词： Basis The Elements computer vision Knowledge Representation computer vision systems Knowledge Bases test sample

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computer vision-based personal identification using 2D maximum intensity projection CT images

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EUROPEAN RADIOLOGY 2025年 1-11页

作者： Heinrich, Andreas Hubig, Michael Mall, Gita Teichgraeber, Ulf Friedrich Schiller Univ Jena Univ Hosp Dept Radiol Jena Germany Friedrich Schiller Univ Jena Univ Hosp Inst Forens Med Jena Germany

Objectivescomputer vision (CV) mimics human vision, enabling the automatic comparison of radiological images from recent examinations with a vast image database for unique identification. This method offers significant potential in emergencies involving unknown individuals. This study assesses whether maximum intensity projection (MIP) images from thoracic computed tomography (CT) examinations are suitable for automated CV-based personal *** study analyzed 12,465 native CT examinations of the thorax from 8177 individuals, focusing on MIP images to assess their potential for CV-based personal identification in 300 cases. CV automatically identifies and describes features in images, which are then matched to reference images. The number of matching points was used as an indicator of identification *** identification rate was 98.67% (296/300) at rank 1 and 99.67% (299/300) at rank 10, among over 8177 potential identities. Matching points were higher for images of the same individual (7.43 +/- 5.83%) compared to different individuals (0.16 +/- 0.14%), with 100% representing the maximum possible matching points. Reliable matching points were mainly found in the thoracic skeleton, sternum, and spine. Challenges arose when the patient was curved on the table or when medical equipment was present in the *** identification based on MIP images from thoracic CT examinations is highly reliable, even for large CV databases. This method is applicable to various 2D reconstructions, provided anatomical structures are comparably represented. Radiology offers extensive reference images for CV databases, enhancing automated personal identification in *** PointsQuestioncomputer vision-based personal identification holds great potential, but it remains unclear whether maximum intensity projection images from thoracic-CT scans are suitable for this *** intensity projection images of the thorax a

关键词： computer vision systems Computed tomography (X-ray) Emergency care Human identification Thorax

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Deep Learning for vision systems in Construction 4.0: a systematic review

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SIGNAL IMAGE AND VIDEO PROCESSING 2023年第5期17卷 1821-1829页

作者： Ottoni, Andre L. C. Novo, Marcela S. Costa, Dayana B. Univ Fed Reconcavo Bahia Technol & Exact Ctr Cruz Das Almas Brazil Univ Fed Bahia Elect Engn Grad Program Salvador Brazil Univ Fed Bahia Dept Elect & Comp Engn Salvador Brazil Univ Fed Bahia Dept Struct & Construct Engn Salvador Brazil

Recently, the construction industry has been digitizing its production processes, the so-called Construction 4.0, in allusion to the paradigm of the fourth industrial revolution. The application of Deep Learning in computer vision systems has been highlighted in Construction 4.0. Thus, the main contribution of this work is to present a systematic review of Deep Learning for vision systems under Construction 4.0, considering the most cited and most recent journal articles between 2017 and 2021 from Scopus database. For this, a research method selected and analyzed 76 published papers. Six main points were evaluated in the proposed methodology: study area, computer vision applications, Deep Learning methods, hyperparameter tuning, data augmentation, and future work. The following topics stand out as relevant perspectives and directions for continued advancement in this field of research: improving Deep Learning models, increasing the quality of databases, investigating the generality techniques and optimizing processing capacity.

关键词： Deep Learning computer vision systems Construction 4 0

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