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Intelligent Inspection Method for Photovoltaic Modules Based on image processing and Deep Learning 3

Intelligent Inspection Method for Photovoltaic Modules Based...

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3rd International conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Li, Tengda Hua, Min Li, Qin Ict Co. Ltd China USC-SJTU Institute of Cultural and Creative Industry Shanghai Jiao Tong University China Tianjin Branch of FAW-VW China

ISBN: (纸本)9781643684444

The photovoltaic industry is a key strategic initiative in achieving carbon neutrality and emission peak and receives national support as a sunrise industry. The solar cell module is the central part of a solar power generation system, and its production quality and cost have a direct impact on the overall quality and cost of the system. The EL quality inspection is crucial for ensuring the quality of PV modules. However, traditional methods of EL quality inspection, such as manual inspection or machine vision inspection, are found to be inefficient, prone to false detections, and expensive in terms of labour costs. Additionally, these methods may lead to secondary damage to PV modules due to human intervention during the inspection process. Therefore, this paper proposes an intelligent inspection method for PV modules based on image processing and deep learning to improve the efficiency and accuracy of EL QC. The method pre-segments module images using EL image data acquisition and pre-classifies module types based on a priori defect types and then performs secondary detection of defective types of PV module defects using Faster RCNN. The proposed method's effectiveness was verified by the EL images collected from an actual PV module production line. The algorithm model was able to label over 12 common defects with strong reliability and achieve a detection accuracy of over 98%. This greatly improves the efficiency and accuracy of EL detection of PV modules and reduces labour costs while improving the quality of PV module detection. © 2023 The Authors.

关键词： Data acquisition

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Very Efficient Convolutional Neural Network Based on the Discrete Hirschman Transform

Very Efficient Convolutional Neural Network Based on the Dis...

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Asilomar conference on Signals, Systems & Computers

作者： Weiwei Wang victor DeBrunner Linda S. DeBrunner Dingli Xue Hanqing Zhao Ranran Tao

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Convolutional Neural Networks (CNNs) play a crucial role in computer vision and machine learning applications, but they are often associated with high computational demands. To tackle this challenge, researchers have turned to the Fast Fourier Transform (FFT) for spectral convolution to help reduce complexity. However, the Discrete Hirschman Transform (DHT) has emerged as a more efficient alternative for performing linear convolutions. In this study, we introduce a novel CNN methodology based on the principles of the DHT. Our experimental results highlight the impressive efficiency of this approach, significantly lowering both computational complexity and processing time. Additionally, we implement the DHT-based method in hardware to validate its performance in real-world applications, demon-strating its effectiveness in practical scenarios.

关键词： Computer vision Fast Fourier transforms Convolution image processing machine learning Hardware Convolutional neural networks Computational complexity Low latency communication Field programmable gate arrays

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Multi-person Pose Estimation with Multi-Attention Mechanism

Multi-person Pose Estimation with Multi-Attention Mechanism

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Qian Shao Yeqin Shao School of Transportation and Civil Engineering Nantong University Nantong China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In recent years, multi-person pose estimation has emerged as a prominent research direction in the field of computer vision, holding significant importance in applications such as human-computer interaction, action analysis, and virtual reality. However, traditional methods are often complex and inefficient, particularly during the feature fusion process, which can lead to the loss of critical information and increased errors under occlusion and complex poses. To address this, multiple attention modules were introduced in the early stages of the network to enhance the modeling of dependencies between key points, ultimately overcoming the limitations of conventional heatmaps through a coordinate classification approach. The design utilizing multiple attention modules achieves a balance between maintaining a lightweight structure and improving accuracy. Furthermore, by introducing a multi-attention mechanism, information loss is reduced, thereby enhancing the model's robustness in handling occlusion and other challenges in complex scenarios. Compared to existing advanced methods, the approach presented in this paper achieves an average precision increase of 1.5 percentage points on the COCO dataset.

关键词： Heating systems Solid modeling Computer vision Accuracy Computational modeling Pose estimation virtual reality Feature extraction Robustness Load modeling

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Metal and Metal Oxide Nanoparticle image Analysis Using machine Learning Algorithm 5th

Metal and Metal Oxide Nanoparticle Image Analysis Using Mach...

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5th EAI International conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Bannigidad, Parashuram Potraj, Namita Gurubasavaraj, Prabhuodeyara Department of Computer Science Rani Channamma University Karnataka Belagavi India Department of Chemistry Rani Channamma University Karnataka Belagavi India

ISBN: (纸本)9783031283239

Nanomaterials are used in almost every field of engineering. Synthesis techniques and conditions greatly affect the properties of synthesized nanomaterials. Identifying the nanomaterial from FESEM and TEM images with bare eyes is an exceedingly impossible task. Digital image processing techniques play a vigorous part in identifying the size and structure, and classifying them precisely helps scientists and investigators to use them in numerous applications. The advantages of digital image processing techniques increase the precision of object recognition in computer vision and pattern recognition. The proposed technique extracts various textural features such as kurtosis, skewness, and entropy from boron, iron, and silver nanoparticle images. The classification is done by using PNN and K-NN classifiers. The K-NN classifier has an accuracy of 80.00% for boron, 86.67% for iron, and 93.33% for the silver nanoparticle images, and the PNN classifier has an accuracy of 86.67% for boron, 93.33% for iron, and 93.33% for silver nanoparticle images. Hence, based on the experimentation, the proposed study suggested that the PNN classification with texture features is the best classifier used to classify the boron, iron, and silver nanoparticle images as compared to the K-NN classifier. Further, the results also are established manually with chemical experts, which proves the exhaustiveness of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Synthesis (chemical)

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Novel view Synthesis from a Single RGBD image for Indoor Scenes

Novel View Synthesis from a Single RGBD Image for Indoor Sce...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Congrui Hetang Yuping Wang Carnegie Mellon University Pittsburgh PA America University of Michigan Ann Arbor MI America

In this paper, we propose an approach for synthesizing novel view images from a single RGBD (Red Green Blue-Depth) input. Novel view synthesis (NVS) is an interesting computer vision task with extensive applications. Methods using multiple images has been well-studied, exemplary ones include training scene-specific Neural Radiance Fields (NeRF), or leveraging multi-view stereo (MVS) and 3D rendering pipelines. However, both are either computationally intensive or non-generalizable across different scenes, limiting their practical value. Conversely, the depth information embedded in RGBD images unlocks 3D potential from a singular view, simplifying NVS. The widespread availability of compact, affordable stereo cameras, and even LiDARs in contemporary devices like smartphones, makes capturing RGBD images more accessible than ever. In our method, we convert an RGBD image into a point cloud and render it from a different viewpoint, then formulate the NVS task into an image translation problem. We leveraged generative adversarial networks to style-transfer the rendered image, achieving a result similar to a photograph taken from the new perspective. We explore both unsupervised learning using CycleGAN and supervised learning with Pix2Pix, and demonstrate the qualitative results. Our method circumvents the limitations of traditional multi-image techniques, holding significant promise for practical, real-time applications in NVS.

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Control of Technical Parameters of Industrial Products Through machine vision 43rd

Control of Technical Parameters of Industrial Products Throu...

引用

43rd IBIMA conference on Artificial intelligence and machine Learning, IBIMA-AI 2024

作者： Gheorghita, Vlad National University of Science and Technology POLITEHNICA Bucharest Romania

ISBN: (纸本)9783031774928

During the production process, a lot of undesirable or faulty products are also created. Since these products cannot be used or distributed on the market, they must be discarded. Product integrity and quality inspection, which is also a result of production process efficiency and optimization, is one of the most important procedures in modern industrial applications. The quickest and most efficient way to make sure products meet the quality requirements is through an automated verification process. These systems are considered as essential by some applications because replacing them with human employees would necessitate a substantial workforce and related costs. This research provides a mechanical experimental system that will be able to verify fast and control the most important parameters of various products using the artificial intelligence approach and machine vision techniques. The cost of the designed system is low and the structure used is clear. The demonstration of the utility of the created product was carried out on a toothed wheel with many essential technical characteristics, a widely used landmark in the field of manufacturing engineering. image processing techniques are used such as threshold function, calibration, pattern matching, measure. The experimental model designed is easy to implement and can represent a transition towards the application of modern industry 5.0 principles in different processing companies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： machine vision

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Detection and Identification of Digital Display Meter of Distribution Cabinet Based on YOLOv5 Algorithm 3rd

Detection and Identification of Digital Display Meter of Dis...

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3rd International conference on Neural Computing for Advanced applications, NCAA 2022

作者： Zhou, Yanfei Zhang, Yunchu Wang, Chao Sun, Shaohan Wang, Jimin Shandong Jianzhu University Jinan250101 China Shandong Key Laboratory of Intelligent Buildings Technology Jinan250101 China

ISBN: (纸本)9789811961410

Aiming at the problem of low recognition accuracy of digital display meter readings when the inspection robot performs inspection tasks, a YOLOv5-based digital display meter detection and recognition algorithm for distribution cabinets is proposed. For the digital display meter image captured by the inspection robot spherical camera, the YOLOv5 model is used to locate the target character area. After scale normalization, image correction, filtering and noise removal and other image pre-processing operations, the character recognition is completed in combination with the traditional machine vision algorithm, and the reading results are automatically output. For the characteristics of digital tube characters, the threading method, support vector machine algorithm and PaddleOCR algorithm are used for comparison, and a suitable algorithm model is selected to recognize numeric, alphabetic and decimal point characters. The experimental results show that the accuracy of detecting and identifying digital display meters using the YOLOv5 model and PaddleOCR algorithm is 95.3%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Expoliting Confidence-Based Model Fusion for Boosting image Classification Accuracy

Expoliting Confidence-Based Model Fusion for Boosting Image ...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Xinjian Jiang Department of Computer Science and Technology Nanjing University Nanjing China

In the realm of deep learning, the traditional approach has been to train specialized models for individual tasks, which, although effective, is resource-intensive. The advent of large, universal models has mitigated this issue by offering multitask capabilities, reduced training time, and lower computational costs. However, these generalized models often underperform on specific tasks compared to specialized models. This paper introduces an innovative ensemble approach that integrates specialized and generalized models, specifically focusing on Contrastive Language–image Pretraining (CLIP) and EfficientNet. This work proposes three fusion strategies: Weighted Voting, Confidence Comparison, and Fully Connected Network Fusion, and evaluate them using the CIFAR-100 dataset. The ensemble model significantly outperforms individual models, achieving an adjusted accuracy of up to 0.848. The paper also introduces a novel evaluation metric, Confidence-Accuracy Correlation, to assess the reliability of model confidence. The findings could revolutionize ensemble learning by making it more adaptive and suited for real-world applications, thereby pushing the boundaries of possibility in artificial intelligence.

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Hardware -Efficient FPGA-Based. Approximate Multipliers for Error -Tolerant Computing 21

Hardware -Efficient FPGA-Based. Approximate Multipliers for ...

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21st International conference on Field-Programmable Technology (ICFPT)

作者： Yao, Shangshang Zhang, Liang Natl Univ Def Technol Changsha Peoples R China

ISBN: (数字)9781665453363

ISBN: (纸本)9781665453363

With the increasing demand for data processing, approximate computing is widely used in various fault-tolerant applications such as image processing, computer vision and machine learning. These applications also require a huge number of multiplication operations. In this paper, we are mainly oriented to the softcore approximate multiplier which is implemented on FPGA via encoding the INIT parameter values in the Look-Up Table (LUT) primitives. Three approximate multipliers with associated carry chain are presented in the manner of reducing LUTs from proposed exact multiplier. An approximate multiplier without carry chain is also presented to further reduce the multiplier's critical path delay and power consumption. We also present an accuracy configurable adder to build high -order approximate multipliers for architectural space exploration. The resolution of the state-of-the-art Mean Relative Error Distance (AIRED) and Power -Delay Product (PDP) pareto front is improved and the approximate multiplier we proposed achieves 24.4%, 52.9% and 56.4% reduction in latency, area, and power over the soft multiplier IP core, respectively. Finally, we apply the proposed approximate multiplier design to image processing and convolutional neural networks (CNNs). Compared to advanced approximate multipliers, it offers less energy consumption and area while remaining acceptable qualities. Our designs are open sourced at h ttps://,g,i thu h. corriN aosh an gsh an g96/FPGAbased_approx_mult to assist further reproducing and development.

关键词： Error-Tolerant computing approximate multipliers image processing

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An Improved Reversible Data Hiding Scheme Based on Referred Frequencies for a VQ Compressed Index

An Improved Reversible Data Hiding Scheme Based on Referred ...

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machine vision, image processing and Imaging Technology (MviPIT), International conference on

作者： Min-Shiang Hwang Kanza Fatima Yu-Lun Wang Chi-Shiang Chan Chia-Chun Wu Department of Computer Science & Information Engineering Fintech and Blockchain Research Center Asia University Taichung Taiwan Department of Computer Science & Information Engineering Asia University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

Nowadays, we usually compress images before uploading them to social media. However, images on social media can easily be copied, so embedding secret messages in compressed images has become increasingly popular. There are many compression methods, such as Huffman, VQ, ZIP, AMBTC, RAR, JPEG, etc. In this article, we propose an improved data hiding in VQ compression method to achieve better capacity and high quality. Experimental results show that our data-hiding approach is practical.

关键词： Steganography image coding Social networking (online) Vector quantization Bit rate Transform coding Imaging Indexes

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