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JOURNAL OF THE ASABE 2023年第4期66卷 839-849页

作者： Qin, Jianwei Hong, Jeehwa Cho, Hyunjeong van Kessel, Jo Ann S. Baek, Insuck Chao, Kuanglin Kim, Moon S. USDA ARS Beltsville Agr Res Ctr Environm Microbial & Food Safety Lab Beltsville MD 20705 USA Natl Agr Prod Qual Management Serv Expt Res Inst Gimcheon Si Gyeongbuk Do South Korea

A novel multimodal optical sensing system was developed for automated and intelligent food safety inspection. The system uses two pairs of compact point lasers and dispersive spectrometers at 785 and 1064 nm to realize dual-band Raman spectroscopy and imaging, which is suitable to measure samples generating low- and high-fluorescence interference signals, respectively. Automated spectral acquisition can be performed using a direct-drive XY moving stage for solid, powder, and liquid samples placed in customized well plates or randomly scattered in standard Petri dishes (e.g., bacterial colonies). Three LED lights (white backlight, Uv ring light, and white ring light) and two miniature color cameras are used for machine vision measurements of samples in the Petri dishes using different combinations of illuminations and imaging modalities (e.g., transmission, fluorescence, and color). Real-time image processing and motion control techniques are used to implement automated sample counting, positioning, sampling, and synchronization functions. System software was developed using LabvIEW with integrated artificial intelligence functions able to identify and label interesting targets instantly. The system capability was demonstrated by an example application for rapid identification of five common foodborne bacteria, including Bacillus cereus, E. coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella spp.. Using a machine learning model based on a linear support vector machine, a classification accuracy of 98.6% was achieved using Raman spectra automatically collected from 222 bacterial colonies of the five species grown on nutrient nonselective agar in 90 mm Petri dishes. The entire system was built on a 30x45 cm(2) breadboard, enabling it compact and portable and its use for field and on-site biological and chemical food safety inspection in regulatory and industrial applications.

关键词： Artificial intelligence Automated sampling Bacteria Food safety machine learning machine vision Raman Sensing

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Adversarial Learning Improves vision-Based Perception from Drones with Imbalanced Datasets

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2023年第8期20卷 489-507页

作者： Dolph, Chester v. Ippolito, Corey Glaab, Louis J. Logan, Michael J. Tran, Loc D. Allen, B. Danette Alam, Mahbubul Li, Jiang Iftekharuddin, Khan NASA Langley Res Ctr Aeronaut Syst Engn Branch Hampton VA 23681 USA NASA Langley Res Ctr Intelligent Syst Div Hampton VA 23681 USA NASA Langley Res Ctr Intelligent Flight Syst Hampton VA 23681 USA Hitachi Amer Ltd Ind Artificial Intelligence Lab Res & Dev Santa Clara CA 95054 USA Old Dominion Univ Dept Elect & Comp Engn Norfolk VA 23529 USA Old Dominion Univ Grad Res Program Norfolk VA 23529 USA Old Dominion Univ Grad Program Norfolk VA 23529 USA

This work proposes a vision-based perception algorithm that combines image-processing-based detection and tracking of aerial objects with convolutional neural networks (CNNs) integrated for classification of general aviation aircraft, multirotor small uncrewed aerial systems (SUAS), fixed-wing SUAS, and birds to enable improved onboard avoidance algorithm decision making. Furthermore, we integrate adversarial learning during the training of the CNNs and evaluate performance with class balanced and imbalanced datasets because this maximizes the utility of resource-expensive flight experiments to collect aviation datasets. We compare our proposed CNN with adversarial learning (CNN+ADvL) model with a state-of-the-art CNN as well as a you only look once (YOLO, v4) model retrained (YOLO v4 aircraft) on the same data. The CNN+ADvL trained on the imbalanced dataset achieves the highest 10-fold cross-validation classification accuracy of 76.2% for aircraft and birds for all ranges while achieving 87.0% aircraft classification accuracy, meeting proposed self-assurance separation distances derived from Federal Aviation Administration (FAA) guidelines. In comparison, the CNNs achieved 74.4% 10-fold cross-validation classification accuracy for aircraft and birds as well as 83.4% accuracy for the aircraft, meeting proposed self-assurance separation distances derived from FAA guidelines. Furthermore, we demonstrate that the integration of adversarial learning improves the classification performance for the perception of aerial objects using a class imbalanced dataset.

关键词： Unmanned Aerial vehicle Convolutional Neural Network image processing Aviation Non Cooperative Collision Avoidance System machine Learning Flight Testing Bird Strike Computer vision

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Real-time monitoring of molten zinc splatter using machine learning-based computer vision

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JOURNAL OF INTELLIGENT MANUFACTURING 2025年第5期36卷 3399-3425页

作者： O'Donovan, Callum Giannetti, Cinzia Pleydell-Pearce, Cameron Swansea Univ Fac Sci & Engn Fabian Way Swansea SA1 8EN Wales

During steel galvanisation, immersing steel strip into molten zinc forms a protective coating. Uniform coating thickness is crucial for quality and is achieved using air knives which wipe off excess zinc. At high strip speeds, zinc splatters onto equipment, causing defects and downtime. Parameters such as knife positioning and air pressure influence splatter severity and can be optimised to reduce it. Therefore, this paper proposes a system that converges computer vision and manufacturing whilst addressing some challenges of real-time monitoring in harsh industrial environments, such as the extreme heat, metallic dust, dynamic machinery and high-speed processing at the galvanising site. The approach is primarily comprised of the Counting (CNT) background subtraction algorithm and YOLOv5, which together ensure robustness to noise produced by heat distortion and dust, as well as adaptability to the highly dynamic environment. The YOLOv5 element achieved precision, recall and mean average precision (mAP) values of 1. When validated against operator judgement using mean average error (MAE), interquartile range, median and scatter plot analysis, it was found that there was more discrepancy between the two operators than the operators and the *** research also strategises the deployment process for integration into the galvanising line. The model proposed allows real-time monitoring and quantification of splatter severity which provides valuable insights into root-cause analysis, process optimisation and maintenance strategies. This research contributes to the digital transformation of manufacturing and whilst solving a current problem, also plants the seed for many other novel applications.

关键词： Galvanisation Steel manufacturing Computer vision Deep learning

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A review of ground camera-based computer vision techniques for flood management

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COMPUTERS AND CONCRETE 2024年第4期33卷 425-443页

作者： Jun, Sanghoon Jang, Hyewoon Kim, Seungjun Lee, Jong-Sub Jung, Donghwi Korea Univ Hyper Converged Forens Res Ctr Infrastruct Seoul 02841 South Korea Korea Univ Dept Civil Environm & Architectural Engn Seoul 02841 South Korea Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTvs) or unmanned aerial vehicles (UAvs) have been examined for flood management (FM). Many computer vision (Cv) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent Cv approaches that utilize UAv images or remote sensing data, less effort has been devoted to studies that have focused on CCTv data. In addition, few studies have distinguished between the main research objectives of Cv techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of Cv applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes Cv techniques for aspects of FM using ground camera (e.g., CCTv) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted Cv techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a Cv model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate Cv model for any FM purpose.

关键词： CCTv computer vision flooding image processing machine learning segmentation

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Organ Localization and Detection in Sow’s Using machine Learning and Deep Learning in Computer vision

Organ Localization and Detection in Sow’s Using Machine Lea...

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作者： Almadani, Iyad Mohd Eid H. The University of Memphis

学位级别：M.S., Master of Science/Master of Surgery

The objective of computer vision research is to endow computers with human-like perception to enable the capability to detect their surroundings, interpret the data they sense, take appropriate actions, and learn from their experiences to improve future performance. The area has progressed from using traditional pattern recognition and image processing technologies to advanced techniques in image understanding such as model-based and knowledge-based vision. In the past few years, there has been a surge of interest in machine learning algorithms for computer vision-based applications. machine learning technology has the potential to significantly contribute to the development of flexible and robust vision algorithms that will improve the performance of practical vision systems with a higher level of competence and greater generality. Additionally, the development of machine learning-based architectures has the potential to reduce system development time while simultaneously achieving the above-stated performance improvements. This work proposes utilizing a computer vision-based approach that leverages machine and deep learning systems to aid the detection and identification of sow reproduction cycles by segmentation and object detection techniques. A lightweight machine learning system is proposed for object detection to address dataset collection issues in one of the most crucial and potentially lucrative farming applications. This technique was designed to detect the vulvae region in pre-estrous sows using a single thermal image. In the first experiment, the support vector machine (SvM) classifier was used after extracting features determined by 12 Gabor filters. The features are then concatenated with the features obtained from the Histogram of oriented gradients (HOG) to produce the results of the first experiment. In the second experiment, the number of distinct Gabor filters used was increased from 12 to 96. The system is trained on cropped image windows and us

关键词： Computer vision Gabor filter machine learning Object localization Support vector machine

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A Breast Cancer Prognosis Model using PyRadiomics and image Segmentation from MRI data 7

A Breast Cancer Prognosis Model using PyRadiomics and Image ...

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7th International Conference on machine vision and applications (ICMvA)

作者： Sonia, Moirangthem Kalita, Indrajit Devi, Debashree SOA Univ Dept CAI & ML Bhubaneswar Odisha India Boston Univ Comp & Data Sci Boston MA 02215 USA IIIT Guwahati Dept CSE Gauhati India

ISBN: (纸本)9798400716553

Breast cancer is a global illness that primarily affects women from young to middle-aged;and precision medicine and tailored treatment are still in need of efforts, to accommodate early diagnosis and prompt treatment. Due to its benefits in gathering rich characteristics for diagnosis and prognosis, radiomics in medical imaging has emerged as a popular research trend that provides vital information about textural and spatial features (specifically tumors/lesions). In this work, the concept of radiomics (both handcrafted and deep-learning based) is investigated for the prognosis of patients with breast cancer, and to determine the course of treatment that can be provided. To achieve this, an open-source package, named Pyradiomics is implemented on RIDER Breast MRI data to extract around 107 hand-crafted radiomics features. Following this, a U-NET-based segmentation model is employed to extract the deep features with four different pre-trained backbone architectures trained on the imageNet dataset, namely ResNet-50, EfficientNet-B3, Inception-ResNet-v2, and MobileNetv2. Among these, U-NET with ResNet-50 has achieved the highest AUC score of 0.7052, outperforming the rest considered and state-of-the-art techniques.

关键词： Breast cancer prognosis MRI data image segmentation Radiomics U-NET model

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Segmenting Sonar images with an Enhanced OTSU Algorithm 2

Segmenting Sonar Images with an Enhanced OTSU Algorithm

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2nd International Conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Yang, Fan University of Jinan School of Electrical Engineering Jinan250022 China

ISBN: (纸本)9798350331417

Sonar image segmentation technique is crucial for underwater target tracking, among other things. Due to the undersea environment's influence, noise is easily absorbed, which leads to a poor tracking performance. In order to address the issues with the traditional one-dimensional and two-dimensional OTSU algorithms, which have a slow computation time and poor anti-noise performance, this paper proposes a fast OTSU segmentation algorithm based on wavelet boosting and analyses the algorithmic principles and implementation steps of one-dimensional OTSU algorithm, two-dimensional OTSU algorithm and its fast algorithm, fast algorithm with the variance as the eigenvalue, and maximum entropy algorithm. On the basis of this, the aforementioned algorithms are used to perform image segmentation on four photos. The segmentation findings demonstrate the method's quick processing speed and strong noise resistance, which have significant implications for sonar image segmentation applications. © 2023 IEEE.

关键词： image segmentation OTSU second generation wavelet transform sonar images

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An Advanced Multi-Scale Feature Compression using Selective Learning Strategy for video Coding for machines

An Advanced Multi-Scale Feature Compression using Selective ...

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2023 IEEE International Conference on visual Communications and image processing, vCIP 2023

作者： Yoon, Yong-Uk Han, Gyu-Woong Lee, Jooyoung Jeong, Se Yoon Kim, Jae-Gon Korea Aerospace University Department of Electronics and Information Engineering Gyeonggi-do Goyang-si Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of Korea Aerospace University School of Electronics and Information Engineering Gyeonggi-do Goyang-si Korea Republic of

ISBN: (纸本)9798350359855

machine vision-based applications have witnessed widespread adoption in diverse fields. Efficiently processing and compressing the vast amounts of video data collected by machines is crucial for these applications. To address this need, the Moving Picture Experts Group (MPEG) is developing a new' video coding standard known as video Coding for machines (vCM), specifically optimized for video consumed by machines in vision applications. This paper proposes an advanced multi-scale feature compression (advMSFC) method with a selective learning strategy (SLS) for the Feature Compression for vCM (FCvCM). By applying the SLS, the proposed method converts multi-scale features into a single-scale feature arranged based on channel-wise importance, enabling adaptive feature channel truncation based on the QP. The truncated feature is efficiently compressed using the latest video codec, versatile video Coding (vvC). The proposed method outperforms the vCM feature anchor in instance segmentation, object detection, and object tracking tasks, achieving significant Bjontegaard delta-rate (BD-rate) gains. The adaptability of our method using a single trained model for various QPs shows promise for efficient feature compression in machine vision applications. © 2023 IEEE.

关键词： Computer vision

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Object Detection at Edge Using TinyML Models

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SN Computer Science 2024年第1期5卷 11页

作者： Dharani, Andhe Kumar, S. Anupama Patil, Peethi N. Department of MCA R.V. College of Engineering Bengaluru India Affiliated to Visvesvaraya Technological University Belagavi India

With the penetration of IoT across sectors, image classification becomes a critical issue if the computations have to be done at the edge. The evolution of low-cost devices with powerful processing for any vision-based systems leads to the next requirement of machine learning for imaging with reduced latency and reliability along with data security. Running the energy hungry computer vision techniques which need frequent memory access may not be the solution. This paper deliberates the works carried out which can be deployed at the end devices, such as mobiles, microcontrollers for image computing at edge. The approach used is by leveraging the TinyML optimized features for low latency and energy efficiency. This paper deals with sample data worked for classification on ML methods, namely, FOMO and MobilenNetSSD which are converted from the Tensorflow to lite version, using the edge Impulse platform. The results discussed are taken from the deployed TinyML model on mobile phone. The outcomes along with the accuracy are also discussed. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： machine learning Object detection TinyML

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machine vision based automated inspection approach for clutch friction disc (CFD)

Machine vision based automated inspection approach for clutc...

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International Conference on Advances in Materials and Mechanical Engineering. (ICAMME)

作者： Kaushik, Sujata Jain, Anjali Chaudhary, Tina Chauhan, N. R. IGDTUW MAE Dept Delhi 110035 India

machine vision is a technology and method used to provide automated image-driven analysis in applications such as inspection, process control, and guidance, and is very popular in industries nowadays. Computer/machine vision has been extensively developed and used in production to achieve precise automatic control. This paper presented an image processing approach, a subset of machine vision, for the visual inspection system of the Clutch Friction Disc (CFD) produced for 2 wheelers. image processing is used to inspect different parts of the CFD. After previous operations of production, a part enters the inspection system, where the geometry and size of the part are inspected, and then image processing technology is used to decide to accept or reject the product. This paper presented the work constructed using a python program with OpenCv which aims to identify the major defects in clutch friction plates, by using different image processing techniques. With the proposed approach decision can be made automatically that whether the processed part will be accepted or rejected and then will be identified as "Ok tested" and "Faulty" pieces. Copyright (C) 2022 Elsevier Ltd. All rights reserved.

关键词： machine vision Automated inspection image processing Python OpenCv Clutch friction plates (CFD)

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