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2024 International Conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Li, Yanan Wang, Hua Li, Yongbo Guangdong University of Science and Technology Guangdong Dongguan523668 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

To achieve target detection and defect recognition in power inspection images, an image processing and recognition algorithm based on deep learning is proposed. This algorithm mainly adopts an improved Faster-RCNN model, combined with HSI color feature extraction, for real-time image processing and identification of various conventional faults. Firstly, the collected power inspection images are normalized, converting the RGB colors of the images to HSI colors. By traversing each pixel in HSI space, it is determined whether there is a fault in the pixel based on the color characteristics of the image. Secondly, a DenseNet network is constructed and connected to the RoIAlign layer and prediction layer. Finally, the identification of target defects is achieved through Faster-RCNN. Simulation results show that the algorithm can accurately identify fault defects with a precision of up to 94.60%, indicating a broad application prospect in power equipment defect recognition. © 2025 SPIE.

关键词： RGB color model

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Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2022年第4期236卷 617-627页

作者： Garcia, Gabriel Rodriguez Michau, Gabriel Ducoffe, Melanie Gupta, Jayant Sen Fink, Olga Swiss Fed Inst Technol Swiss Fed Inst Technol Zurich Switzerland Airbus AI Res Toulouse Midi Pyrenees France

The ability to detect anomalies in time series is considered highly valuable in numerous application domains. The sequential nature of time series objects is responsible for an additional feature complexity, ultimately requiring specialized approaches in order to solve the task. Essential characteristics of time series, situated outside the time domain, are often difficult to capture with state-of-the-art anomaly detection methods when no transformations have been applied to the time series. Inspired by the success of deep learning methods in computer vision, several studies have proposed transforming time series into image-like representations, used as inputs for deep learning models, and have led to very promising results in classification tasks. In this paper, we first review the signal to image encoding approaches found in the literature. Second, we propose modifications to some of their original formulations to make them more robust to the variability in large datasets. Third, we compare them on the basis of a common unsupervised task to demonstrate how the choice of the encoding can impact the results when used in the same deep learning architecture. We thus provide a comparison between six encoding algorithms with and without the proposed modifications. The selected encoding methods are Gramian Angular Field, Markov Transition Field, recurrence plot, grey scale encoding, spectrogram, and scalogram. We also compare the results achieved with the raw signal used as input for another deep learning model. We demonstrate that some encodings have a competitive advantage and might be worth considering within a deep learning framework. The comparison is performed on a dataset collected and released by Airbus SAS, containing highly complex vibration measurements from real helicopter flight tests. The different encodings provide competitive results for anomaly detection.

关键词： Unsupervised fault detection time series encoding helicopters vibrations CNN

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real-time Social Distance maintaining using image processing and deep learning

Real-Time Social Distance maintaining using Image Processing...

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2021 International Conference on Computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

作者： Senthil Murugan, K.R. Kavinraj, G. Mohanaprasanth, K. Ragul, Krishnan B. Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Quite possibly the most and best measures to contain the new popular episode is that the upkeep of the purported Social Distancing. The widespread Covid infection 2019 (COVID-19) has carried worldwide emergency with its lethal spread to very 180 countries. This paper shows the procedure for social separating recognition utilizing profound figuring out how to check the space between individuals to relieve the effect of this Covid pandemic, this content proposes a profound learning based system for robotizing the assignment of observing social removing utilizing reconnaissance video. Revelation instrument was made to form people mindful of manage an ensured distance. An information from the camera source utilized as video graph, as necessities be the open-source object territory model maintained the YOLOv3 object divergence model to disconnect people from the inspiration and deep-sort thanks to affect oversee follow the apparent individuals with the assistance of bobbing boxes and dispatched IDs. The space between people is much of the time evaluated and any defiant pair of people inside the introduction are shown with red packaging and line. The future procedure was affirmed on a pre-recorded audiovisual of people by walking around and about. The outcome shows that the proposed strategy is in a situation to work out the social removing measures between various individuals inside the video. These system are consistently moreover developed as an ID mechanical assembly continuously application. © Published under licence by IOP Publishing Ltd.

关键词： deep learning

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Use of deep learning in nano image processing through the CNN model

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ADVANCES IN NANO RESEARCH 2022年第2期12卷 185-195页

作者： Xing, Lumin Liu, Wenjian Liu, Xiaoliang Li, Xin Wang, Han Shandong First Med Univ Affiliated Hosp 1 Jinan 250014 Shandong Peoples R China Shandong Prov Qianfoshan Hosp Jinan 250014 Shandong Peoples R China City Univ Macau Macau 999078 Peoples R China Shandong Univ Polit Sci & Law Jinan 250014 Shandong Peoples R China Chinese Acad Sci Zhuhai Inst Adv Technol Zhuhai 519000 Guangdong Peoples R China

deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

关键词： convolutional neural network CT image deep learning image processing lung cancer

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Enhancing Palm Recognition Accuracy Through image processing deep learning Techniques 2

Enhancing Palm Recognition Accuracy Through Image Processing...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Mercy, J. Sheela Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350373110

Palm recognition systems play an important role in biometric authentication;however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Mechanism for image Capturing, processing, and Evaluation for various programs using deep learning 4

Automated Mechanism for Image Capturing, Processing, and Eva...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vijay Anand, K. Sai Sharan, P. Nehith, M. Rajashekar, V.S. R.M.K. Engineering College Thiruvallur India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This paper presents an exploration of object detection, a critical application in deep learning characterized by its robust feature learning and representation capabilities. It focuses on typical methodologies such as YOLO, detailing their underlying principles and evaluating their real-time performance and detection accuracy. The existing model, trained for human detection, requires manual data updates and lacks the ability to identify duplicate or inappropriate images. Our project addresses these challenges by developing an enhanced detection system capable of identifying image types, ensuring image clarity by filtering out blurred images and detecting duplicates ensuring, for example, that an image from Day 1 of a workplace is not reuploaded for subsequent days. Additionally, the system is designed to detect trained objects more accurately. This project proposes the development of software that, during image capture, integrates geotagging, pixel analysis for blur detection, duplicate identification, and image type classification to improve the overall efficiency and accuracy of object detection. © 2024 IEEE.

关键词： Adversarial machine learning

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Experimental Analysis of Malarial Parasites Infection by using deep learning based on Medical image processing Logic 3

Experimental Analysis of Malarial Parasites Infection by usi...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Meenakshisundaram, N. Sajiv, G. Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331508456

Malaria remains a significant global health challenge, particularly in resource-limited regions, necessitating accurate and rapid diagnostic tools. This study introduces deepMalariaNet, a deep learning model developed for detecting and classifying malarial parasites using the 'Malaria Parasite image - Malaria Species' dataset from Kaggle. The model employs Residual Attention Mechanisms and Parallel Convolutional Stacks (PCS) to improve diagnostic accuracy by focusing on critical image regions and capturing multi-scale features. Experimental results demonstrate that deepMalariaNet achieves 98.5% accuracy for binary classification (infected vs. non-infected) and 95.2% for multiclass classification (species identification). The model's robustness is validated through 10-fold cross-validation and ablation studies, and it outperforms state-of-the-art models such as ResNet-50 and DenseNet-121 in both accuracy and inference time. deepMalariaNet shows significant promise for real-time malaria detection in clinical settings, contributing to early and accurate diagnosis, which is crucial for effective malaria control and treatment. © 2025 IEEE.

关键词： Photointerpretation

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Tactile image processing and Shape Detection Method of Array Tactile Sensor for Surgical Robot Based on deep learning 17th

Tactile Image Processing and Shape Detection Method of Array...

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Niu, Xinyao Ju, Feng Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819607761

The rapid evolution of modern medical technology has propelled surgical robots to the forefront of surgical innovation. These robots offer unprecedented accuracy and safety in procedures, empowering surgeons with advanced technical capabilities to tackle complex medical challenges. In tumor treatment, surgical robots excel in precise tumor tissue resection, minimizing damage to healthy tissue and reducing surgical risks. However, accurately identifying tumor boundaries and depth remains challenging due to limitations of traditional palpation methods. This study aims to develop a deep learning model for real-time tumor boundary detection within surgical robot systems. By processing array tactile sensor image data, this model will enable precise boundary and depth detection, enhancing surgical robot recognition and operational capabilities. This advancement promises improved surgical success rates, patient survival rates, and reduced risks of complications, ultimately enhancing patient treatment outcomes and quality of life. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： deep learning

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Integrating image processing Techniques with deep learning Network Models to Detect and Identify Defects on the Surface of Tomato

Integrating Image Processing Techniques with Deep Learning N...

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Do Van, Dinh Sao Do University Hai Duong03500 Viet Nam

ISBN: (纸本)9798350327816

Before export, fruit should be classified to improve quality, meet customer requirements and increase product value. This article proposes a method to identify defects on the surface of tomato skin using image processing techniques combined with deep learning models. The identification method includes the following main steps: (i) data collection (image of tomato: green, ripe, diseased, scratched), (ii) image labeling, (iii) data file division, (iv) model training, (v) selection and using models. The results of using Faster R-CNN model combining Resnet-10l and testing on YOLOv5 to identify and classify tomatoes that met and failed export for high accuracy (95.3 %) and met get real time. © 2023 IEEE.

关键词： deep convolutional neural network deep learning Faster R-CNN tomato export classification YOLO YOLOv5

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A Survey on Multi-Classification of Pulmonary Diseases Using image processing and deep learning Algorithms 4

A Survey on Multi-Classification of Pulmonary Diseases Using...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Uma Maheswari, V. Mandala, Niveditha Reddy Ponnur, Roopika Chaitanya Bharathi Institute of Technology Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350387063

Multi-classification of pulmonary diseases poses a significant challenge, particularly when diseases share similar radiological presentations like lung cancer, pneumonia, and COVID-19. While chest CT scan images are essential for diagnosis, manual interpretation by radiologists is time-consuming and difficult, even for experts. deep learning algorithms offer promise in this domain, demonstrating high accuracy in classifying these diseases from CT images. This review examines recent advances in using deep learning for multiclassification of pulmonary diseases. It discusses various preprocessing techniques, deep learning architectures to enhance model performance. Additionally, a comparative analysis of different deep learning models on publicly available CT datasets is presented. The findings suggest that deep learning models could serve as valuable tools for multiclassification of pulmonary diseases, potentially improving diagnostic accuracy and efficiency. Nonetheless, further research is required to develop and validate these models on large, diverse datasets and ensure their robustness in real-world clinical settings. © 2024 IEEE.

关键词： Lung cancer

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