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International Conference on Science Technology Engineering and Management (ICONSTEM)

作者： S Sujitha Harshika vinoth Kumar K Hemavathi v Disha M Aamna Nafiza Department of EEE New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The agricultural landscape is evolving, demanding innovative solutions to enhance productivity while ensuring the welfare of livestock. Farmguard introduces an advanced Automated Animal Detection and Monitoring System designed to revolutionize traditional farm management practices. Leveraging cutting-edge sensor technology, computer vision, and machine learning algorithms, Farmguard offers real-time, non-invasive monitoring of animal behavior, health, and movement within farm premises. This system operates seamlessly, utilizing a network of strategically placed sensors and cameras to track and identify individual animals. Through sophisticated image processing and AI-powered algorithms. This technology aims to minimize conflicts by providing early warnings about animal intrusion, enabling timely intervention strategies. By leveraging Arduino's capabilities and image processing techniques.

关键词： Technological innovation Machine learning algorithms Tracking image processing Wildlife Real-time systems Agriculture

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Disease Detection of Leaf Review Collection using Deep Learning

Disease Detection of Leaf Review Collection using Deep Learn...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Saranya, L. Ashwin Kannan, E. Jagan, A. Jayasuriya, R. vignesh, v. Department of Electronics and Communication Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Nowadays, automated systems are preferred in almost all industries. In particular, automated systems are highly preferred in agriculture industry to maintain the crops healthy and for irrigation purposes. The proposed study analyses the process of disease detection in a sample leaf image by utilizing the Open Cv module. The proposed model recognizes the diseased region of the provided sample leaf image and displays the disease's description. Open Cv plays a key role in determining the disease name by using the image processing algorithms, which will identify the disease present in leaf images by simply using the disease name. © 2022 IEEE.

关键词： Deep learning

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Ingenious Waste Management: Implementing Machine Learning in Waste Segregation 6

Ingenious Waste Management: Implementing Machine Learning in...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Asha, v. Nithya, B. Govindaraj, M. Brunda, T.M. Hiremath, Ashwini Jayaprakash, B. New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798350306613

Waste management is a pressing global issue, and the need for efficient waste separation processes is becoming increasingly important. Incorporating Machine Learning techniques with waste separation has yielded promising results and revolutionized the waste separation process. However, this research paper highlights the gaps that exist in the understanding and efficacy of machine learning for waste separation. This study provides an overview of state-of-the-art techniques for waste separation using machine learning, including the use of computer vision, sensor technology, and neural networks. This paper portrays case studies with empirical data from various waste management establishments to demonstrate the potential advantages of machine learning for recycling and waste classifying. Although progress has been made, this article highlights some significant gaps in our knowledge. These gaps include the need for standardized datasets, optimization of algorithms for real-world scenarios, the impact of technology on the environment, and the socio-economic aspects of integrating automation into waste management. The essential and most important tasks of intelligent mechanisms in the digital age is waste separation. Segregation of waste is necessary to apply proper disposal and waste management strategies. The existing systems use drones to identify waste by using deep learning, image processing, GPS, and GSM approaches, locating and notifying the authorities about them. The improvement made is in the analysis and utilize multi-object detection and picture classification to accomplish trash segregation. Waste management may be effectively accomplished with higher accuracy through Machine Learning practices which therefore considerably lowers labor expenses. © 2024 IEEE.

关键词： image Detection Machine Learning Prediction Sensor Waste Segregation

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How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?

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KNOWLEDGE-BASED systems 2023年第1期272卷

作者： Mousavirad, Seyed Jalaleddin Schaefer, Gerald Zhou, Huiyu Moghadam, Mahshid Helali Univ Beira Interior Covilha Portugal Loughborough Univ Dept Comp Sci Loughborough England Univ Leicester Sch Informat Leicester England RISE Res Inst Sweden Boras Sweden Malardalen Univ Vasteras Sweden

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu's method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds. Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.(c) 2023 Elsevier B.v. All rights reserved.

关键词： image processing Multi -level thresholding image segmentation Metaheuristic algorithms Optimisation

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Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

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MULTIDIMENSIONAL systems AND SIGNAL processing 2022年第2期33卷 301-326页

作者： Satyanarayana, K., v Rao, N. Thirupathi Bhattacharyya, Debnath Hu, Yu-Chen RAGHU Engn Coll A Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India Vignans Inst Informat Technol A Dept Comp Sci & Engn Visakhapatnam 530049 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur 522502 Andhra Pradesh India Providence Univ Dept Comp Sci & Informat Management 200Sec 7Taiwan Blvd Taichung 43301 Taiwan

This paper is mainly aimed at the decomposition of image quality assessment study by using Three Parameter Logistic Mixture Model and k-means clustering (TPLMM-k). This method is mainly used for the analysis of various images which were related to several real time applications and for medical disease detection and diagnosis with the help of the digital images which were generated by digital microscopic camera. Several algorithms and distribution models had been developed and proposed for the segmentation of the images. Among several methods developed and proposed, the Gaussian Mixture Model (GMM) was one of the highly used models. One can say that almost the GMM was playing the key role in most of the image segmentation research works so far noticed in the literature. The main drawback with the distribution model was that this GMM model will be best fitted with a kind of data in the dataset. To overcome this problem, the TPLMM-k algorithm is proposed. The image decomposition process used in the proposed algorithm had been analyzed and its performance was analyzed with the help of various performance metrics like the variance of Information (vOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). According to the results, it is shown that the proposed algorithm achieves the better performance when compared with the previous results of the previous techniques. In addition, the decomposition of the images had been improved in the proposed algorithm.

关键词： Probabilistic Framework Bacterial Culture Mixture Density Estimation Expectation Maximization Segmentation Quality Metrics

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DEvELOPMENT AND OPTIMIZATION OF A DEEP-LEARNING-BASED EGG-COLLECTING ROBOT

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TRANSACTIONS OF THE ASABE 2021年第5期64卷 1659-1669页

作者： Li, Guoming Chesser, Gary Daniel Huang, Yanbo Zhao, Yang Purswell, Joseph L. Mississippi State Univ Dept Agr & Biol Engn Mississippi State MS 39762 USA USDA ARS Genet & Sustainable Agr Unit Mississippi State MS USA Univ Tennessee Dept Anim Sci Knoxville TN 37901 USA USDA ARS Poultry Res Unit Mississippi State MS USA

Manual collection of floor eggs in cage-free hen housing systems is time-consuming and laborious. The objectives of this study were to (1) develop a robot to automatically collect floor eggs and (2) optimize the performance of recognizing and picking eggs with the robot. The robot consisted of a deep-learning-based egg detector, a robot arm, a twofinger gripper, and a hand-mounted camera. The deep-learning model, You Only Look Once (YOLO) v3, was embedded in the vision system to detect and locate eggs on a simulated litter floor in real-time. image processing algorithms (e.g., cropping, erosion, etc.) were implemented for the detection and provided the robot with centroid coordinates, orientation, and axis lengths of the detected eggs, so that the gripper could be manipulated with an appropriate angle and opening width to grasp the detected eggs. For optimization, the YOLO v3 model was retrained with a dataset of floor eggs and achieved >93% performance in detecting and locating eggs. The kernel size of 65. 65 pixels for erosion and dilation in image processing assisted in extracting the geometry features of eggs with the least remaining noises. Among the tested materials, soft grouting sponge attached to the gripper had the highest success rates for egg picking. The robot achieved 92% to 94% success in picking white and brown eggs. In sum, the developed egg-collecting robot can be relied on for picking floor eggs to assist in precision management of cage-free hen housing systems.

关键词： Floor egg image processing Laying hen Robot arm Soft gripper YOLO v3

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Methods and algorithms for Constructing Super Resolution for a Sequence of images under Applicative Noise

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JOURNAL OF COMPUTER AND systems SCIENCES INTERNATIONAL 2021年第3期60卷 465-476页

作者： Ivankov, A. Yu. Savvin, S. v. Sirota, A. A. Voronezh State Univ Voronezh Russia

The problem of constructing multiframe superresolution (SR) based on processing a sequence of low-resolution (LR) images in conditions of applicative noise (AN) is considered. The latter appear in the form of distributed areas of false or anomalous observations in LR images and are considered as an additional factor in reducing the quality of the original images, characterized by an irregular arrangement of LR or zero-resolution areas. The existing methods for solving this problem are analyzed using models of spin glasses and their varieties, as well as models of random Markov fields. The authors describe a method based on the use of recurrent algorithms for the optimal conditional linear filtering of a sequence of LR images in combination with superpixel segmentation and Expectation-Maximization-clustering (EM-clustering) to identify areas affected by AN. The synthesis of conditionally linear filtering algorithms is considered both in the usual and in the adaptive setting, taking into account the possible uncertainty regarding the processing parameters and registration means. An experimental study is carried out to compare algorithms on sets of test images. The analysis of the experimental results shows certain advantages of the developed approach for the synthesis of algorithms for constructing SR in an adaptive setting, which consists in increasing the accuracy and structural similarity of high-resolution (HR) image restoration in comparison with analogs.

关键词： Control Robotics Mechatronics

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Comparing the Effectiveness and Performance of image processing algorithms in Face Recognition

Comparing the Effectiveness and Performance of Image Process...

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International Carpathian Control Conference (ICCC)

作者： Hussam M. Rostum József vásárhelyi Institute of Automation and Info-Communication University of Miskolc Miskolc Hungary

ISBN: (数字)9798350350708

ISBN: (纸本)9798350350715

Many institutions have recently embraced biometric security solutions, utilizing biological measurements to safeguard against fraudulent activities, theft, and various security threats. Face recognition technology holds a pivotal role within the realm of bio-metric security systems, serving purposes such as authentication, monitoring, individual identification, and identity verification. This article aims to delve into the examination of facial recognition systems grounded in deep learning. This focus arises due to the intricate nature of the process and the existence of numerous hurdles and variables that impact algorithm performance. The objective here is to illuminate the foremost challenges that real-world systems encounter, often overlooked in previous research. Additionally,under these challenges, the article will conduct a comparative analysis of the performance of prominent facial recognition algorithms, namely vGGFace, FaceNet, and ArcFace. This academic approach will allow to make informed choices when selecting the most suitable algorithms for specific applications.

关键词： Deep learning Employee welfare Databases Face recognition image processing Lighting Biology

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image-Based Analysis of Plate Separation and Clearance Distribution in Wet Clutches

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9270页

作者： Pointner-Gabriel, Lukas Flamm, Simon voelkel, Katharina Stahl, Karsten Tech Univ Munich Gear Res Ctr FZG TUM Sch Engn & Design Dept Mech Engn D-85748 Garching Germany

A fundamental understanding of wet clutches' drag loss behavior is essential for designing efficient clutch systems. It has been widely recognized that the separation behavior immediately after disengaging the clutch and the resulting clearance distribution influence the drag loss behavior. However, these influencing factors have yet to be systematically investigated. Therefore, this study aimed to experimentally investigate the effects of plate separation and clearance distribution on drag loss behavior under different operating conditions and modes. For this purpose, image series of an operating clutch system were recorded and subsequently analyzed using image processing algorithms to evaluate the movements of the plates. Based on this, the effects on drag loss behavior were analyzed. The investigations were carried out on a clutch system used in an industrial application. The measurements show that the axial movements of the plates comprise main and superimposed non-periodic movements of much a smaller amplitude. The separation of the plates is primarily driven by the applied differential speed so that the set total clearance is only utilized mainly in the higher differential speed range. The separation behavior, therefore, decisively influences the drag loss behavior. The plates can even remain in contact in the low differential speed range. The investigations also showed that the separation behavior and, thus, the drag loss behavior can be improved by using waved plates, especially in the low differential speed range. It was also found that a high plate number and a large set total clearance support a non-uniform clearance distribution. Based on the investigations conducted, it is possible to expand our fundamental knowledge of separation behavior and clearance distribution, allowing for a reduction in the drag losses of wet clutches. The findings can thus contribute to the development of low-loss and compact clutch systems.

关键词： applied image processing wet clutches drag losses plate separation

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Anisotropic Diffusion Method on Multiple Domain Noisy images: A Recommendation

Anisotropic Diffusion Method on Multiple Domain Noisy Images...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), International Conference on

作者： Palanisamy v Muthukumaran Malarvel Thangakumar J Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

Noise poses a maj or challenge to image processing, making accurate analysis and interpretation more difficult. Anisotropic diffusion algorithms specifically tailored for noisy images across several domains are examined in this paper. The efficiency of anisotropic diffusion in lowering noise in various image datasets is evaluated in-depth in this study. Based on comprehensive study and experimentation, this work presents real proof of the efficacy of this method in decreasing noise while preserving significant image properties in multiple domains. In comparison to the MP and MPM models, the experimental findings show that the suggested model performs quite well.

关键词： Anisotropic magnetoresistance image processing image edge detection Noise Noise reduction Anisotropic Data engineering

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