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A Method for Detecting Water Leakage in Heating Pipes Based on Infrared and Visible images 2

A Method for Detecting Water Leakage in Heating Pipes Based ...

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2nd international conference on Signal image processing and Communication, ICSIPC 2022

作者： Wang, ZhiJie Ma, ZhiJun He, ZiFeng Tan, XueJing Zheng, YaFeng Spic Hebei Electric Power Co. Ltd Shijiazhuang China Spic Hebei Electric Power Co. Ltd Thermal Branch Shijiazhuang China State Nuclear Electric Power Planning Design & Research Institute Co. Ltd

ISBN: (纸本)9781510657694

Aiming at the complex environment in the inspection of underground heating pipes, including high-temperature flanges, valves, and stale water stains on the surface of heat pipes, a method for detecting water leakage in heating pipelines combining thermal infrared and visible images is designed. Temperature anchor frame detection is performed on thermal infrared images, and deep learning is used for visible image for water stain anchor frame detection. This paper proposes a detection method for determining the degree of correlation and high temperature point to determine the water outlet point based on the position coincidence degree, which excludes the infrared interference caused by the high temperature of the flange and valve, and the visible light interference caused by the old water stains, and has a good detection result for the leakage of the heat pipe. © 2022 SPIE.

关键词： computer vision

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Fruit and Vegetable Recognition System Using Deep Transfer Learning Approach 2nd

Fruit and Vegetable Recognition System Using Deep Transfer L...

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2nd NIELIT's international conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Dkhar, Albinus Bania, Rubul Kumar Department of Computer Application North-Eastern Hill University Meghalaya Tura Campus794002 India Department of Computer Science Birangana Sati Sadhani Rajyik Vishwavidyalaya Assam Golaghat785621 India

ISBN: (纸本)9789819736003

Effectively categorizing an extensive range of fruits and vegetables can be accomplished by leveraging computer vision and image processing techniques. This article presents the development of an automated system for recognizing fruits and vegetables, employing deep transfer learning models. To build our image dataset, we combined images sourced from the publicly available "Fruit 360 dataset" with images we captured ourselves. The dataset encompasses 26 different types of fruits and 34 distinct vegetable categories. For this research, we primarily leveraged three key techniques: Simple CNN, ResNet50, and MobileNetV2, to carry out the final classification task. The achieved accuracy rates for these models were as follows: 76.67% for Simple CNN, 78.63% for ResNet50, and an impressive 96.52% for MobileNetV2. Furthermore, we have harnessed the power of the MobileNetV2 model to create a novel Android smartphone application within Android Studio. This application is designed to automatically classify fruits and vegetables. Users can take advantage of this app to identify and categorize fruits and vegetables using either their device’s camera or images from their gallery. One notable feature of this app is its ability to provide information in three different languages: English, Khasi, and Garo, ensuring accessibility for a wide range of users. With this innovative application, anyone can easily and accurately identify a variety of fruits and vegetables, making it a valuable tool for both consumers and enthusiasts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Vegetables

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Optimized Quantum Circuits in Quantum image processing Using Qiskit 12

Optimized Quantum Circuits in Quantum Image Processing Using...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Boreiri, Zahra Azad, Alireza Norouzi Majd, Nayereh University of Tehran Department of Engineering Science Tehran Iran University of Tehran College of Engineering Faculty of Engineering Science Department Tehran Iran

ISBN: (纸本)9781665412162

Quantum image representation is an essential component of quantum image processing and plays a critical role in quantum information processing. Flexible Representation of Quantum images (FRQI) presents pixel colors and associated locations as a state to represent images on quantum computers. A fundamental part of the quantum image processing system is quantum image compression (QIC), which is utilized to maintain and retrieve binary images. This compression allows us to minimize the number of controlled rotation gates in the quantum circuits. This paper designed optimized quantum circuits and simulated them using Qiskit on a real quantum computer based on minimum boolean expressions to retrieve the 8×4 binary single-digit images. To demonstrate the feasibility and efficacy of quantum image representation, quantum circuits for images were developed using FRQI, and quantum image representation experiments were done on IBM Quantum Experience (IBMQ). We were able to visualize quantum information by doing the quantum measurement on the image information that we had prepared. Without utilizing this method, the number of controlled rotation gates is equal to the number of pixels in the image;however, we showed that by using the QIC algorithm, we could decrease the number of gates significantly. On these images, the maximum and minimum compression ratios of QIC are 90.63% and 68.75%, respectively. © 2022 IEEE.

关键词： image representation

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Assertive vision Using Deep Learning and LSTM 2

Assertive Vision Using Deep Learning and LSTM

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2nd international conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Bhadauria, Siddhant Singh Bisht, Dharmendra Poongodi, T. Yadav, Suman Avdhesh Galgotia University Computer Science And Engineering Greater Noida India School Of Computer Science And Engineering Galgotias University Greater Noida India Amity University Department Of Computer Science And Engineering Greater Noida India

ISBN: (纸本)9781665466431

image captioning is an elemental which needs correct understanding of images and also the ability of generating description sentences with proper and proper structure. The captioning of an image must identify the objects within the image, their actions, and their relationships, in addition as any silent features that will be absent from the image. This has been a challenging task within the field of computing throughout the years. Using any tongue sentences to automatically create a picture description may be a difficult issue. This paper explores the numerous picture captioning models that are available. Within the past few years, the matter of generating descriptive sentences automatically for images has garnered a rising interest in tongue processing and computer vision research. The LSTM units are intricate and naturally sequential in nature. Our model can study long-time period visual-language interactions the usage of ancient and information about the future in a high-level semantic domain via combining two different LSTM networks and a deep CNN. We used two publicly available datasets to demonstrate the utility of this strategy: Flickr8k and Flickr30k. Flickers has an API that may be used for image collections. We will use the APIs to search out images and explore for them by tag as an example, you'll search images for common day items/places and buildings like burger, temple, etc. We provide multiple approaches to picture captioning supported deep learning similarly as distinct evaluation strategies during this survey work. Since present deep learning may be a black-box model, it's critical to look at the impact on each module to completely comprehend the model. © 2022 IEEE.

关键词： Visual languages

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Heart Disease Diagnosis using Deep Learning 2

Heart Disease Diagnosis using Deep Learning

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2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kumar, Ashish Jain, Dhyanendra Singh, Prashant Das, Sanjoy Abes Engineering College Department of CSE-AIML Ghaziabad India Sunder Deep Engineering College Department of Computer Science and Engineering Ghaziabad India Indira Gandhi National Tribal University Department of Computer Science Engineering Manipur India

ISBN: (纸本)9798350369960

Developments for automation and advanced computing in the area of medical data processing has outcome with different new learning techniques. Deep learning has evolved as an advanced approach in machine learning applied to different old and new area of applications. Deep learning approaches have evolved as supervised, semi-supervised and un-supervised mode applied for different real time applications. The approach has shown a significant usage for image processing, computer vision, medical diagnosis, robotic and control operation application. Among various usage of machine learning approaches for automation, medical diagnosis has been observed as a new upcoming application. The criticality of data processing, response time, and accuracy in decision, tends the learning system more complex in usage for medical diagnosis. This paper outlines the developments made in the area of medical diagnosis and deep learning application for heart disease diagnosis. The application, database and the learning system used in the automation process is reviewed and outlined the evolution of deep learning approach for medical data analysis. © 2023 IEEE.

关键词： Deep learning approach Diagnosis and decision Heart disease diagnosis

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ML based Implementation for Documents Forensic and Prediction of Forgery using computer vision Framework 2

ML based Implementation for Documents Forensic and Predictio...

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2nd international conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Pratapagiri, Sreenivas Madugula, Sujatha Kiran, Siripuri Rao, V. Chandra Shekhar Venkatramulu, S. Phridviraj, M.S.B. Kakatiya Institute of Technology and Science Department of Cse Telangana Warangal India Jyothishmathi Institute of Technology and Science Department of Cse Telangana Karimnagar India

ISBN: (纸本)9781665400527

Facial Recognition, Biometric Verification, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking are just a few of the fields benefiting from breakthroughs in computer vision and Optical image processing right now. These applications prepare for dealing with numerous applications for breaking down and determining through the use of images and live video. There are several workouts that make use of computer vision, including checking, face recognition, movement recognition, and object placement. Due to advances in long-distance informal communication stages such as Facebook and Instagram, the amount of picture data being generated has increased. image and video manipulation software usage poses a huge risk to Facebook because of the manipulated images that users submit to their social network. These sorts of pictures are habitually referred to as phony and utilized in noxious ways like rousing viciousness and passing. Need to confirm the sketchy pictures before make a move. It is extremely difficult to guarantee photograph validness because of the force of photograph controls. Picture arrangement can be dictated by picture measurable strategies. The strategy of picture duplication is utilized to hide missing regions. © 2022 IEEE.

关键词： Geometrical optics

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Research on the Construction of image processing Model Based on computer vision Algorithm

Research on the Construction of Image Processing Model Based...

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Control, Electronics and computer Technology (ICCECT), IEEE international conference on

作者： Wang Yi Economic Law School of Law Shandong University of Technology Zibo Shandong China

ISBN: (数字)9798350380958

ISBN: (纸本)9798350380965

This research focuses on constructing an efficient image processing model, which is rooted in computer vision algorithms, to ameliorate image distortion and optimize visual display systems. The article initially discusses the significance of image distortion correction and its processing flow, encompassing detailed steps ranging from image preprocessing, model construction, parameter estimation, to pixel coordinate transformation. Furthermore, the design approach of computer vision display systems is explored, covering the theory of light field reconstruction, software and hardware design essentials, ensuring high fidelity in image presentation. Through multiple experiments conducted on datasets of distorted images, the effectiveness of the model in correcting distortion and enhancing image quality is empirically demonstrated, with the experimental results strongly supporting the feasibility and superiority of the model. This research holds positive implications for advancements and practical applications in the field of image processing.

关键词： computer vision Visualization Computational modeling image processing Display systems Distortion Solids

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An AI Enabled Framework for MRI-based Data Analytics for Efficient Brain Stroke Detection 2

An AI Enabled Framework for MRI-based Data Analytics for Eff...

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2nd IEEE international conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Patil, Anitha Govindaraj, Sureshkumar Pondicherry University Department of Computer Science UT Karaikal Puducherry609605 India

ISBN: (纸本)9798350315905

These days, it seems that medical image processing is a good fit for deep learning-based models. There is a potential basis for exploiting healthcare services thanks to advancements in deep learning and pre-trained models in computer vision applications. According to the World Health Organization (WHO), a clinical decision support system (CDSS) based on deep learning has the potential to advance the state of the art in medical image analysis. Timely management is essential due to the high death and disability rates associated with stroke. Timely study of brain imaging allows for rapid medical action. Existing research on MRI-based brain stroke analysis requires a refined and enhanced strategy to reap the potential advantages of deep learning, and MRI is discovered to provide additional possibilities for medical picture analysis. As may be seen in the material, a lot of focus has been placed here. In this study, we present the Deep Automated Brain Stroke Detection Framework (DABSDF), a deep learning-based system for detecting strokes in brain MRI. The Deep Convolutional Neural Network-based Pipeline for Brain Stroke Detection is a method we proposed (DCNNP-BSD). To test the effectiveness of the proposed framework and its algorithms, a prototype application has been developed in the Python data science environment. We evaluate our model against current deep learning models. The effectiveness of the various models on the MRI dataset varies widely. In terms of performance, VGG16 fares the worst while the suggested model, DCNNP-BSD, fares the best. With a dice similarity coefficient of 0.8822979, 0.8554022 sensitivity, 0.99595785 specificity, and 0.97774774 accuracy, the suggested CNN-based deep learning model beat the state-of-the-art. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Real-Time Crop Analysis with Enhanced image Classification Using Deep Learning and Fuzzy Logic Techniques 2

Real-Time Crop Analysis with Enhanced Image Classification U...

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2nd international conference on computer vision and Internet of Things, ICCVIoT 2024

作者： Kanimozhi, S. Deepika Roselind, J. Shah, Neal Sai Ramesh, L. School of Computer Science and Engineering VIT University Chennai India School of Electronics Engineering VIT University Chennai India Department of Computer Science and Engineering St. Joseph's Institute of Technology Chennai India

ISBN: (纸本)9781837242672

The proposed research presents a smart irrigation system is designed to optimize agricultural water management using a robotic framework that integrates deep learning and fuzzy logic for real-time crop classification and decisionmaking. It employs a combination of VGG-19 and a fusion model RTCropNet (Real-time crop network), leveraging the strengths of each architecture: VGG-19 for robust feature extraction and the RTCropNet fusion for enhanced spatial localization and fine-grained crop classification. Pre-processing is performed through advanced fuzzy logic techniques to improve image quality, enhancing classification accuracy across diverse crop types. Evaluation metrics indicate that the RTCropNet model achieves a training accuracy of 80.15% and a validation accuracy of 81.77%, with precision, recall, and F1 scores of 0.83, 0.82, and 0.82, respectively, outperforming traditional methods on complex agricultural imagery. Comparative analysis shows the model's superiority over EfficientNet-B3, DenseNet-121, and MobileNetV2, particularly in handling high-resolution spatial details, while maintaining efficiency in real-time conditions. This integration of deep learning with fuzzy logic and sensor data represents a novel approach in precision irrigation, offering a dynamic, datadriven response system that adjusts irrigation based on real-time environmental conditions, distinguishing it from conventional SOTA approaches. © The Institution of Engineering & Technology 2024.

关键词： Deep learning

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Real Time Object Detection Using Deep Learning 2

Real Time Object Detection Using Deep Learning

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2nd IEEE international conference on vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Sornalakshmi, M. Sakthimohan, M. Elizabeth Rani, G. Aravindhan, V. Surya, B.K. Devadharshni, M. Arulmigu Kalasalingam College of Arts and Science Dept of Computer Applications Tamilnadu India KGiSL Institute of Technology Department of ECE Tamilnadu Coimbatore India

ISBN: (纸本)9798350347982

Real-time object detection using deep learning has emerged as a burgeoning field of study due to its potential for a wide range of applications, including autonomous driving, robotics, and surveillance systems. The primary goal of this method is to identify interesting objects in real-world situations quickly and accurately. By utilizing convolutional brain organizations (CNNs) for highlight extraction and article identification, advanced learning-based strategies have demonstrated exceptional outcomes in this area. CNNs are trained on large-scale image datasets to learn discriminative features that capture object appearance and context effectively. The features extracted by the CNN are then used to detect objects using a detection algorithm. The Region-based Convolutional Neural Network (R-CNN) framework is one popular approach, which first proposes a set of candidate regions and then applies a CNN to each region to extract features for classification and localization. Faster CNN architectures such as Single Shot Detector (SSD) and You Only Look Once (YOLO), as well as hardware acceleration strategies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs), have been proposed as ways to improve real-time performance. These methods allow for high frame rates and real-time object detection, making them suitable for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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