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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VII"

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image processing-Based Dust Detection for Solar Panels

Image Processing-Based Dust Detection for Solar Panels

引用

Smart systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), International conference on

作者： Sree Sowndarya Barani K Keerthana S Hariharasudhan S Mohana SundaramN B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Tamilnadu India Electrical and Electronics Engineering Kumaraguru College of Technology Tamilnadu India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This study focuses on dust detection in solar panels by utilizing image processing techniques. Dust detection helps in forecasting the maintenance needs and ensures system reliability. Inefficient maintenance practices of solar panels lead to increased downtime and decreased energy production. Implementation of dust detection holds importance in ensuring consistent power output. In the existing methodologies, Artificial Neural Network algorithms are implemented for dust detection. Artificial Neural Network algorithms show a considerably lower accuracy in image processing due to overfitting and complexity of image data. In the proposed system, a comparative analysis is performed by implementing Multi-Layer Perceptron technique and Dense Net algorithm. Using Multi-Layer Perceptron, a Sequential model is built with one flatten layer and two dense layers. It shows an accuracy of 88%. The Dense Net algorithm was also implemented on the processed image dataset and it shows an accuracy of 98%. This helps in increasing the accuracy and minimizing error. The Thonny IDE is employed for the implementation of the models, with validation conducted using hardware components such as Raspberry Pi, solar panels, and camera modules. Real-time images are captured to assess the performance. The utilization of the Convolution Neural Network algorithm, specifically Dense Net, for dust detection has led to enhanced system accuracy and efficiency.

关键词： Temperature sensors Temperature distribution Accuracy image processing Fault detection Voltage Hardware

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Real-Time Dehazing and Defogging: A Comprehensive Analysis for Single image Haze and Fog Removal

Real-Time Dehazing and Defogging: A Comprehensive Analysis f...

引用

Information Technology, Electronics and Intelligent Communication systems (ICITEICS), IEEE International conference on

作者： Apurva Kumari A. Pavan Kumar B.V.S Tarun Teja Department of Electronics and Communication Engineering B V Raju Institute of Technology Telangana

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Implementing image dehazing and defogging on a Field Programmable Gate Array (FPGA) offers efficiency. Dehazing an image becomes particularly challenging in the presence of fog or haze. However, employing a dark channel prior to dehazing allows the removal of haze particles from the image. Different modules are used in this process, such as Dynamic Atmospheric Light Estimation (DALE), Scene Recovery (SR), and Transmission Map Estimation (TME). The FPGA runs these modules in hardware and produces effective outcomes by employing image processing algorithms. In this case, using FPGA technology offers a number of benefits. The dehazing process can be accelerated by using FPGA's built-in parallel processing capabilities to execute numerous operations at once. Furthermore, FPGA implementations provide better throughput and reduced latency in comparison to conventional approaches, making them well-suited for real-time applications such as image dehazing and D

关键词： Estimation Transportation Parallel processing Logic gates Throughput Real-time systems Safety

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An Accurate and Time-Efficient Subtractor by Cross Format Coding in Stochastic Computing

An Accurate and Time-Efficient Subtractor by Cross Format Co...

引用

IEEE Asia Pacific conference on Circuits and systems (APCCAS)

作者： Zhang, Zhihuai Zhang, Weiqian Xiong, Shisheng Zhao, Yudi Fudan Univ Sch Informat Sci & Technol Micronano Syst Ctr Shanghai Peoples R China Beijing Informat Sci & Technol Univ Minist Informat Ind Key Lab Informat & Commun Syst Beijing Peoples R China

ISBN: (纸本)9781665450737

Different from binary computation, stochastic computation (SC), as a new paradigm, uses stochastic bit stream (SBS) to encode data. By simplifying computing elements, the circuit area can be greatly reduced. SBS can be generated by a stochastic number generator (SNG) with a variety of formats. In this work, we use unipolar (UP) and bipolar (BP) formats to optimize the traditional SC subtractor, which is named the UP-to-BP Subtractor (UBS). A new cross format coding (CFC) method is proposed for stochastic computing, which combines the UP and BP format, and is applied to Sobel edge detection in image processing algorithms. The fault tolerance and detection efficacy of the proposed CFC method and conventional binary computing are compared in this paper. By using the CFC method, the detected F-Score is improved by 0.15(23%). If the F-score remains unchanged, the processing speed can be about 10 times faster.

关键词： stochastic computing stochastic subtractor image processing edge detection

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Research on the Detection Algorithm of Rural Road Surface Damage Based on image processing 2nd

Research on the Detection Algorithm of Rural Road Surface Da...

引用

2nd International conference on image, Vision and Intelligent systems, ICIVIS 2022

作者： Ye, Pei Jiang, Tao Zhan, Hui Wuchang Institute of Technology HB WuHan China Wuhan University of Technology HB WuHan China

ISBN: (纸本)9789819909223

China's new rural construction project has developed rapidly. The rural landscape has changed a lot. The scale of the rural roads is getting larger and larger. The maintenance cost of rural road diseases is also very large. People use computer technology and artificial intelligence technology to improve the detection effect of road viruses in rural areas. This paper has collected a large number of pictures of rural road surface diseases. The types of disease include: heet damage, potholes, cracks and peeling. We use the SSD algorithm to process and identify these images. with some results, We have achieved some results, but the overall identification rate is not particularly high. To further improve the identification effect, we propose an improved SSD algorithm. We found that the improved SSD algorithm also had some results in the identification of rural road surface diseases. We compare the two algorithms, and we find that improving the SSD algorithm has more advantages. The improved SSD algorithm has a higher recognition rate and a faster recognition speed. We believe that this algorithm can provide a new idea for rural pavement disease identification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image processing

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Research on the Application of image Recognition and processing Technology in Integrated Media Information Dissemination

Research on the Application of Image Recognition and Process...

引用

Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Shiyu Pan Jiangsu College of Tourism Yangzhou Jiangsu China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the rapid development of the era of integrated media, the form of information dissemination is becoming more and more diversified, in which the image as an important carrier of information dissemination, its quality and processing efficiency directly affect the dissemination effect. By virtue of its unique advantages in data analysis, image optimization and content distribution, image recognition and processing technology has become an important support means in the field of integrated media information dissemination. This paper firstly analyzes the current situation of the application of image recognition and processing technology in the information dissemination of integrated media; secondly, it discusses the core methods of image recognition and processing technology, including deep learning algorithms, feature extraction technology and real-time image processing strategies; finally, it elaborates the specific application effects of these technologies in the fields of news reporting, advertising and marketing, and live video broadcasting through practical application examples. The study shows that image recognition and processing technology not only improves the intelligent level of integrated media content production, but also provides new possibilities for the innovation of information dissemination mode. In the future, with the further improvement of algorithm performance and the technological integration of multimedia platforms, this field is expected to gain wider application.

关键词： Industries Technological innovation image recognition Face recognition Production Media Real-time systems User experience Optimization Standards

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An Efficient Hardware Accelerator For Lossless Data Compression 5

An Efficient Hardware Accelerator For Lossless Data Compress...

引用

5th International conference on Communications, Signal processing, and their Applications (ICCSPA)

作者： Mahmoud, Adel Farid, Samuel Maged, Mark Mohamed, Othman Karam, Reham Salah, Khaled El-Kharashi, M. Watheq Ain Shams Univ Dept Comp & Syst Fac Engn Cairo Egypt Siemens Digital Ind Software Fremont CA 94538 USA

ISBN: (纸本)9781665482370

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Lossless Data Compression Lossless image Compression Hardware Accelerator Compression algorithms Rice-Golomb Predictive Coding XORing

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Presenting a Method for Gluing Error Detection Using image processing

Presenting a Method for Gluing Error Detection Using Image P...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Moein Shishebor Vali Derhami Masoud Khouri Computer Engineering Department Yazd University Yazd Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

The application of artificial intelligence and image processing for automatic fault detection in industrial settings holds substantial significance. However, image processing systems encounter challenges in achieving accurate automatic detection, including variable lighting conditions, low image quality, and the complexity of observed objects. This study specifically addresses the detection of gluing errors in automotive parts. An improved template matching algorithm is employed to identify boundary errors. The proposed method incorporates SIFT (Scale-Invariant Feature Transform) for feature extraction, allowing for the identification of key local features in images regardless of changes in scale, rotation, or lighting conditions. To address variations in scale and alignment, affine transformations are employed to standardize the images, ensuring consistent dimensions and orientations. After this preprocessing step, brute-force matching techniques are utilized to accurately match the extracted features from the modified images with a target reference, refining the output and enhancing the detection accuracy of improperly glued areas. Results from executing the algorithm on input images demonstrate the high performance of the proposed method in gluing error detection.

关键词： Accuracy Refining Signal processing algorithms Lighting Transforms Signal processing Feature extraction Reliability Standards Automotive engineering

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A Study of Machine Learning and Deep Learning algorithms for Deep Fake images Prediction

A Study of Machine Learning and Deep Learning Algorithms for...

引用

Data Science and Network Security (ICDSNS), International conference on

作者： Padmavati E. Gundgurti K. Baburao Department of Computer Science and Engineering CMR University Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Recent developments in synthetic data have made it feasible to develop high-quality images in a way that humans cannot recognize the difference between images generated through Artificial Intelligence (AI) and real-time images. AI and Machine Learning (ML) in general have shown impressive results recently in a variety of tasks including natural image processing, particularly with the development of Deep Learning (DL). Particularly, image classification in medical images requires a high degree of accountability and reliability. However, previous researches require further analysis, often with a better understanding of the image classification process. To identify and categorize images as either synthetic or natural, this review offers a variety of ML and DL techniques. This survey looks at developments in digital imagery to strengthen these AI systems and increase their ability to perform effectively.

关键词： Deep learning Surveys Machine learning algorithms Reviews Network security Prediction algorithms Real-time systems Reliability image classification Synthetic data

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A HVDC System Fault Diagnosis Method Based on PSO-LightGBM Algorithm

A HVDC System Fault Diagnosis Method Based on PSO-LightGBM A...

引用

International conference on Vision, image and Signal processing (ICVISP)

作者： Zhanjun Huang Maning Zhang Jun Xie Yiting Wang School of Aeronautics Northwestern Polytechnical University Xi'an China National Key Laboratory of Aircraft Configuration Design Xi'An China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Due to the complexity of their structure and the particularity of their application environments, aircraft High-Voltage Direct Current (HVDC) systems are prone to faults, with inter-module failures complicating fault diagnosis. To address this issue, a fault diagnosis method for HVDC systems has been developed. Feature extraction methods were designed for the rectifier, BUCK converter, and inverter, respectively, with the sum-to-amplitude ratio of signals selected as a feature for the rectifier; multi-scale skewness was proposed for the BUCK converter; and the ratio of the signal's average to peak absolute value was chosen for the inverter. Subsequently, the PSO-LightGBM algorithm was proposed, which employs the LightGBM algorithm for classification and utilizes a particle swarm algorithm to optimize the parameters of the LightGBM, establishing the optimal model. The experimental results demonstrate that the proposed method can accurately achieve fault diagnosis in HVDC systems.

关键词： Fault diagnosis Buck converters HVDC transmission Atmospheric modeling Signal processing algorithms Rectifiers Feature extraction Inverters Classification algorithms Aircraft

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Semantic Segmentation and image Quality Assessment of Anterior Segment images for Smartphones 14

Semantic Segmentation and Image Quality Assessment of Anteri...

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14th International conference on Graphics and image processing (ICGIP)

作者： Chen, Yuanhao Huang, Kun Fan, Wen Chen, Qiang Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Med Univ Dept Ophthalmol Affiliated Hosp 1 Nanjing 210029 Peoples R China

ISBN: (纸本)9781510666313;9781510666320

Eye diseases have always been a threat to public health worldwide. Many people suffer from various eye diseases, but there are not enough skilled ophthalmologists to meet the demand for medical care. Thus, finding a method to perform ophthalmic examinations automatically and conveniently is necessary. Although many well-designed ophthalmic diagnosis systems have been proposed to diagnose ophthalmic disorders using artificial intelligence algorithms, they tend to depend on high-quality anterior segment images to perform appropriately. In order to capture high-quality anterior segment images simply with a smartphone, we proposed a system including a semantic segmentation model and an image quality assessment method for anterior segment images. Our proposed segmentation model, namely the multi-task anterior segment image semantic segmentation (MT-ASISS) model, has a designed multi-task learning network structure and achieves an accuracy of 92.63% in Dice and a processing speed of 138ms per frame on smartphones. Our anterior segment image quality assessment method, namely Mixed-Parameters Quality Assessment (MPQA) method, has an accuracy of 92.6% in mean average precision (mAP). The system can help reduce the demand for professional image collecting equipment, share the burden of choosing satisfactory images manually and improve the efficiency of acquiring anterior segment images.

关键词： Anterior Segment image Segmentation Multi-Task Learning image Quality Assessment Lightweight Network

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