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1st International Conference on image processing Machine learning and Pattern Recognition

作者： Huang, Ruijie Wu, Haoran Huang, Jiayi Beijing Normal Univ Hong Kong Baptist Univ United AI FST Beijing Peoples R China

ISBN: (纸本)9798400707032

In this study, a deep learning-based galaxy classification method is discussed. The classification of galaxies is important for understanding the formation and evolution of the universe, and traditional classification methods rely on artificial visual analysis, but in the face of large amounts of data, this method is time-consuming and prone to error. In recent years, automated classification methods, especially using deep learning techniques, have gradually come into focus. deep learning models, particularly convolutional neural networks (CNNS), are capable of automatically extracting and learning complex features in galactic images, enabling efficient and accurate classification. The research plan is to integrate multimodal data, train models with large-scale datasets, and introduce interpretative analysis into the classification process to improve model transparency. Ultimately, the goal is to develop an efficient galactic classification system to support data processing and analysis in the field of astronomy.

关键词： deep learning image classification Galaxy Probability distribution

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PA2E: real-time Anomaly Detection With Hyperspectral Imaging for Food Safety Inspection

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IEEE ACCESS 2024年 12卷 175535-175549页

作者： Lee, Jungi Kim, Myounghwan Yoon, Jiseong Yoo, Kwangsun Byun, Seok-Joo ELROILAB Res & Dev Div Seoul 08389 South Korea

Hyperspectral imaging captures material-specific spectral data, making it effective for detecting contaminants in food that are challenging to identify using conventional methods. In the food industry, the occurrence of unknown contaminants is problematic due to the difficulty in obtaining training data, highlighting the need for anomaly detection algorithms that can identify previously unseen contaminants by learning from normal data. deep learning techniques for anomaly detection have been extended to hyperspectral imaging. However, existing methods often use fully connected layers that do not leverage local spectral similarities or convolutional layers that overlook positional importance due to transform invariance, leading to performance limitations. Furthermore, practical hyperspectral contaminant detection in food requires significant real-time processing capabilities, hindered by the computational inefficiency of convolution operations and the limitations of existing deep neural network architectures. To address these issues, we propose a novel model called Partial and Aggregate Autoencoder (PA2E), which incorporates local information without transform invariance and utilizes masked fully connected layers to achieve high inference speed. Additionally, optimizations such as ReLU culling and layer fusion were applied to the fully connected architecture, dramatically reducing inference time without compromising accuracy. Validation on three hyperspectral datasets demonstrated that PA2E achieved an average detection capability of 29, outperforming the current state-of-the-art methods (average of 23 out of 42) with 2.6 times faster inference speed. PA2E leverages the unique characteristics of hyperspectral data and offers a highly optimized layer structure, making it a promising model for application in the hyperspectral food industry and other hyperspectral domains.

关键词： Hyperspectral imaging Anomaly detection Vectors Feature extraction Data models Aggregates image reconstruction Decoding Training Neural networks Autoencoder real-time neural network hyperspectral imaging anomaly detection food inspection

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A comprehensive study on enhanced QR extraction techniques with deep learning-based verification

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APPLIED INTELLIGENCE 2025年第7期55卷 1-22页

作者： Alam, Nur Sagar, A. S. M. Sharifuzzaman Zhang, Wenqi Jin, Taicheng Dosset, Arailym Dang, L. Minh Moon, Hyeonjoon Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Sejong Univ Dept Intelligent Mechatron Engn Seoul 05006 South Korea Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Informat Technol Da Nang 550000 Vietnam

In the digital age, Quick Response (QR) codes have become essential in sectors such as digital payments and ticketing, propelled by advancements in Internet of Things (IoT) and deep learning. Despite their growing use, there are significant challenges in the accurate extraction and verification of QR codes, particularly in dynamic environments. Traditional methods struggle with issues like variable lighting, complex backgrounds, and counterfeits, which degrade the performance of QR code extraction and verification processes. This paper introduces a comprehensive approach that refines QR code extraction using enhanced adaptive thresholding techniques and incorporates a deep learning framework specifically tailored for robust QR code verification. Our methodology integrates dynamic window size adjustment, statistical weighting, and post-thresholding refinement to ensure precise QR code extraction under varying conditions. The verification process employs the ShuffleNetV2 network to ensure high performance with significantly low processing times suitable for real-time applications. Additionally, our deep learning model is trained on a comprehensive dataset comprising 28,523 images across 24 distinct QR code pattern classes, including variations in lighting, noise, and backgrounds to simulate real-world conditions. Experimental results demonstrate that our proposed methodology outperforms competing techniques in both processing speed and recognition accuracy, achieving a processing time of 0.08 seconds and a validation accuracy of 99.99% in constrained scenarios. Our approach shows an exceptional ability to distinguish real QR codes from counterfeits and highlights the significance and efficacy of our method in addressing contemporary challenges.

关键词： QR code extraction deep learning Feature extraction Verification

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An Adaptive Fuzzy C-Means segmentation and deep learning model for efficient mammogram classification using VGG-Net

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BIOMEDICAL SIGNAL processing AND CONTROL 2024年 88卷

作者： Rathinam, Vinoth Sasireka, R. Valarmathi, K. PSR Engn Coll Dept Elect & Commun Engn Sivakasi Tamil Nadu India Mepco Schlenk Engn Coll Dept Biotechnol Sivakasi Tamil Nadu India

Breast cancer mortality can be prevented by only early, accurate mammography screening and diagnosis. Although CNN-based computer-aided diagnosis (CAD) systems for breast cancer have made tremendous progress recently, accurate identification of mammography lesions is still difficult because of poor signal-to-noise ratio (SNR) and physiological features. In this manuscript, an Adaptive Fuzzy C-Means Segmentation and deep learning Model for Efficient Mammogram Classification Using VGG-Net (AFCM-DCNN) is proposed. The input image is given to Grey Code Approximation Pre-processing (GCAP) algorithm to enhance the quality of image by adjusting the pixel contrast. The preprocessed image is given to Adaptive Fuzzy C-Means (AFCM) algorithm and is applied in segmenting dominant regions in an input image. But in conventional AFCM technique, the centroid values get generated randomly, which consumes more computational time. Hence to enhance the performance of traditional AFCM, centroid value is optimally chosen by means of optimization algorithm. A technique for classifying images called DCNN analyses the input image and categorizes it as either benign, malignant and normal. The method extracts the features of the image and train with VGG-16 Net classifier. The neurons at the output layer have been designed to compute Class Centric Disease Support (CCDS) towards various classes. Accordingly, the mammogram class is identified towards detecting the brain tumor. The performance of the proposed method AFCM-DCNN exhibits higher accuracy of 29.3%, 25.6% and 24.6%, higher sensitivity of 15.4%, 16.6% compared with the existing methods. Therefore, in future work, hope to enhance the performance depending on transfer learning with similar data.

关键词： Adaptive Fuzzy C-Means (AFCM) Breast Cancer deep Convolution Neural Network (DCNN) Grey Code Approximation Pre-processing (GCAP) algorithm Opposition based Cat Swarm Optimization (OCSO) VGG16-Net

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A Transfer learning Approach for Enhancing Road Surface Classification in Intelligent Driving Systems

A Transfer Learning Approach for Enhancing Road Surface Clas...

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2024 IEEE International Conference on Signal processing and Advance Research in Computing, SPARC 2024

作者： Judeson Antony Kovilpillai, J. Jayanthy, S. Syed Mohamed, Sulaiman Mir, Mahmood Hussain Pragya Dhar, Soumi Alliance University Dept. of CSE Bangalore India Sri Ramakrishna Engineering College Dept. of ECE Coimbatore India

ISBN: (纸本)9798350385199

Road surface classification is very critical in the domain of intelligent driving systems as it ensures enhanced safety and comfort in autonomous vehicles. In this paper, a transfer learning approach is proposed for effectively classifying the road surface with enhanced accuracy and real-time performance mitigating the constraints of traditional image processing techniques A deep learning model is developed and trained in this research using an open-source dataset, encompassing 72,400 images capturing distinct road surface characteristics present across varying terrains. This research adopts pre-trained models like Mobilenet, VGG-19 and Resnet101 as feature extractors to optimize and improve the classification performance of the deep learning model significantly. The proposed methodology is evaluated using different key performance metrics such as loss, accuracy, average rate of improvement and change in accuracy, and total training time. The proposed deep learning technique achieved an accuracy of 98.77 %, signifying its critical importance for real-world applications and practical deployment. © 2024 IEEE.

关键词： Adversarial machine learning

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Structural Approach to Convolutional Neural Network Trained With Novel Scaled Matrix image for Pseudo real-time Power Quality Event Monitoring

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IEEE ACCESS 2024年 12卷 130833-130856页

作者： Mishra, Sairam Mallick, Ranjan Kumar Gadanayak, Debadatta Amaresh Nayak, Pravati Sharma, Renu Flah, Aymen Prokop, Lukas Kraiem, Habib Kanan, Mohammad Siksha O Anusandhan Deemed be Univ Dept Elect Engn ITER Bhubaneswar 751030 India Siksha O Anusandhan Deemed be Univ Dept Elect & Elect Engn ITER Bhubaneswar 751030 India Univ Gabes Natl Engn Sch Gabes Energy Proc Environm & Elect Syst Unit Gabes 6029 Tunisia Middle East Univ MEU Res Unit Amman 11831 Jordan Univ Gabes Private Higher Sch Appl Sci & Technol Gabes ESSAT Gabes 6029 Tunisia Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan VSB Tech Univ Ostrava ENET Ctr Ostrava 70800 Czech Republic Northern Border Univ Coll Engn Dept Elect Engn Ar Ar 91431 Saudi Arabia Univ Business & Technol UBT Coll Engn Ind Engn Dept Jeddah 21448 Saudi Arabia

The trend of integrating different distributed generation sources into the existing grid have increased the probability of power quality disturbances to a threatening level. Eventually, detection, protection and mitigation of these disturbances are even more challenging. In this regard, the article presents an intelligent power quality disturbance classification scheme using a 2D convolutional neural network designed from a systematic and structural standpoint. A total of 8 singular, 5 complex power quality events are simulated and voltage data collection is made from a test system designed in MATLAB Simulink environment. The three-phase voltage data is converted to a single signal arrangement through a newly proposed Unique Clark's Transformed Sequence. In addition to that, the scheme completely eliminates the worry of a signal processing stage by proposing a novel scaled matrix image created out of 2-cycle data collected at 6.4kHz sampling frequency that acts as input to the CNN architecture designed in the PYTHON environment. Further, the novelty extended to design a pseudo-real-time setup where the MATLAB environment continuously runs the test system, producing scaled matrix images. These images are saved to a shared directory, enabling a PYTHON loop for prompt event classification through the trained CNN model. The model performance is found to be 100% under ideal conditions. It has also tested under three different noise conditions of 40dB, 30dB and 20dB and obtained an overall accuracy of 98.86% with singular events. Further, the method is also verified for complex and unsymmetrical dataset and found to be equally effective. Additionally, the validation is likewise made with a trivial set of real-time simulated data using OPAL-RT 4510 setup. Finally, the proposed PQ detection scheme is compared with recently published work to express its superiority over other similar studies in terms of classification *** TERMS Convolutional neural network, deep l

关键词： Feature extraction Convolutional neural networks Manuals Power quality Mathematical models Signal processing Data models deep learning Power distribution networks Convolutional neural network deep learning power quality disturbance classification scaled matrix image unique Clark's transformed sequence

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WaveletFormerNet: A Transformer-based wavelet network for real-world non-homogeneous and dense fog removal

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image AND VISION COMPUTING 2024年 146卷

作者： Zhang, Shengli Tao, Zhiyong Lin, Sen Liaoning Tech Univ Sch Elect & Informat Engn Huludao Liaoning Peoples R China Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang Liaoning Peoples R China

Although deep convolutional neural networks have achieved remarkable success in removing synthetic fog, it is essential to be able to process images taken in complex foggy conditions, such as dense or non-homogeneous fog, in the real world. However, the haze distribution in the real world is complex, and downsampling can lead to color distortion or loss of detail in the output results as the resolution of a feature map or image resolution decreases. Moreover, the over-stacking of convolutional blocks might increase the model complexity. In addition to the challenges of obtaining sufficient training data, overfitting can also arise in deep learning techniques for foggy image processing, which can limit the generalization abilities of the model, posing challenges for its practical applications in real-world scenarios. Considering these issues, this paper proposes a Transformer-based wavelet network (WaveletFormerNet) for real-world foggy image recovery. We embed the discrete wavelet transform into the Vision Transformer by proposing the WaveletFormer and IWaveletFormer blocks, aiming to alleviate texture detail loss and color distortion in the image due to downsampling. We introduce parallel convolution in the Transformer block, which allows for the capture of multi-frequency information in a lightweight mechanism. Such a structure reduces computational expenses and improves the effectiveness of the network. Additionally, we have implemented a feature aggregation module (FAM) to maintain image resolution and enhance the feature extraction capacity of our model, further contributing to its impressive performance in real-world foggy image recovery tasks. Through extensive experiments on real-world fog datasets, we have demonstrated that our WaveletFormerNet achieves superior performance compared to state-of-the-art methods, as shown through quantitative and qualitative evaluations of minor model complexity. Additionally, our satisfactory results on real-world dust remov

关键词： image dehazing Convolution neural network Wavelet transform Swin transformer Fog removal

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PDSE-YOLOv8: a lightweight detection method for internal defects in asphalt roads

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SIGNAL image AND VIDEO processing 2024年第12期18卷 8925-8936页

作者： Li, Ning Zhang, Wenliang Liu, Zhaoxu Liu, Kaifeng Wang, Junjie Zhang, Fan Ocean Univ China Dept Civil Engn Qingdao 266100 Peoples R China Shandong Hispeed Grp Co Ltd Innovat Res Inst Jinan 250014 Peoples R China Shandong Hispeed Engn Test CO LTD Jinan 250014 Peoples R China BIM Res Inst China Construct Engn Div 8 Shanghai 200112 Peoples R China

Ground penetrating radar (GPR) is an effective tool for detecting internal defects in asphalt roads due to its non-destructive nature and high resolution. However, detecting defects in GPR images remains challenging, as existing models lack sufficient accuracy and are often complex and redundant. To address these issues, a lightweight real-time detection method based on ground-penetrating radar is proposed in this study. First, a field GPR image dataset of asphalt roads was collected and constructed. To address the limited defect sample data acquired by GPR, an efficient copy-and-paste augmentation method was employed. This method involved copying and pasting defect samples in GPR images while incorporating random scale jitter and position migration operations to generate a sufficient number of real defect samples. Second, the C2f-DSConv module and the SE attention mechanism were designed and introduced based on the YOLOv8 network to improve detection accuracy in the complex background environment of GPR images. Finally, a channel pruning strategy was used to prune the improved YOLOv8 network, reducing model complexity while maintaining detection accuracy. The final model achieves an average detection accuracy of 90.9% and a detection speed of 140.9 FPS. The results show that the proposed method combines both detection accuracy and real-time performance, further advancing the engineering application of internal defect detection in asphalt roads.

关键词： image processing deep learning algorithm Asphalt pavement Internal defect Channel pruning Ground penetrating radar (GPR)

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A deep Reinforcement learning-Based Shortwave Multistation Autonomous Cooperative Direction Finding and Localization Method

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IEEE SENSORS JOURNAL 2024年第23期24卷 40123-40136页

作者： Feng, Qiyue Tang, Tao Wu, Zhidong Zhang, Yunpu Wang, Ding Informat Engn Univ Inst Informat Engn Zhengzhou 450001 Peoples R China

Multistation direction finding and passive localization have a wide range of applications in the fields of information countermeasures and navigation. However, signal detection and recognition based on deep learning require a lot of manual annotation, and the localization algorithm requires a large amount of computation, resulting in poor shortwave signal direction finding and localization in real time. To solve the time-consuming and labor-intensive problem, we propose a shortwave direction finding and localization method based on deep reinforcement learning (DRL). The direction of arrival (DOA) of the source signal can be used to locate the shortwave source, but the result of direction finding is not ideal in the case of the same frequency interference. In the proposed method, a spacetime high-resolution processing method is designed to improve the accuracy of obtaining directional results under the complex shortwave background. The Cramer-Rao lower bound (CRLB)-based reward function is also designed, and a Markov decision process (MDP) is established. The autonomous direction finding and localization environment is trained by deep Q-network (DQN) and double-DQN (DDQN), and the comparison shows that the DDQN performs better compared with DQN. Finally, we save the online model and compare its positioning performance with other classical algorithms. The simulation experiment results and performance tests verify the feasibility of the proposed method.

关键词： deep reinforcement learning (DRL) direction finding and localization double-deep Q-network (DDQN) Markov decision process (MDP) space-time high-resolution time-frequency spectrum

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Research on Defect Diagnosis of Transmission Lines Based on Multi-Strategy image processing and Improved deep Network

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PROCESSES 2024年第9期12卷

作者： Gou, Ming Tang, Hao Song, Lei Chen, Zhong Yan, Xiaoming Zeng, Xiangwen Fu, Wenlong Yichang Elect Power Survey & Design Inst Co Ltd Yichang 443000 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China Hubei Zefeng Elect Power Design Co Ltd Yichang 443002 Peoples R China

The current manual inspection of transmission line images captured by unmanned aerial vehicles (UAVs) is not only time-consuming and labor-intensive but also prone to high rates of false detections and missed inspections. With the development of artificial intelligence, deep learning-based image recognition methods can automatically detect various defect categories of transmission lines based on images captured by UAVs. However, existing methods are often constrained by incomplete feature extraction and imbalanced sample categories, which limit the precision of detection. To address these issues, a novel method based on multi-strategy image processing and an improved deep network is proposed to conduct defect diagnosis of transmission lines. Firstly, multi-strategy image processing is proposed to extract the effective area of transmission lines. Then, a generative adversarial network is employed to generate images of transmission lines to enhance the trained samples' diversity. Finally, the deep network GoogLeNet is improved by superseding the original cross-entropy loss function with a focal loss function to achieve the deep feature extraction of images and defect diagnosis of transmission lines. An actual imbalance transmission line dataset including normal, broken strands, and loose strands is applied to validate the effectiveness of the proposed method. The experimental results, as well as contrastive analysis, reveal that the proposed method is suitable for recognizing defects of transmission lines.

关键词： defect diagnosis multi-strategy image processing morphological analysis generative adversarial network GoogLeNet focal loss

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