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deep learning techniques to detect rail indications from ultrasonic data for automated rail monitoring and maintenance

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ULTRASONICS 2024年 140卷 107314页

作者： Islam, Md Ashraful Olm, Georg Tech Univ Berlin Chair Civil Syst Engn Gustav Meyer Allee 25 D-13355 Berlin Germany

The increasing number of passengers and services using railways and the corresponding increase in rail use has caused the acceleration of rail wear and surface defects which makes rail defect identification an important issue for rail maintenance and monitoring to ensure safe and efficient operation. Traditional visual inspection methods for identifying rail defects are time-consuming, less accurate, and associated with human errors. deep learning has been used to improve railway maintenance and monitoring tasks. This study aims to develop a structured model for detecting railway artifacts and defects by comparing different deep-learning models using ultrasonic image data. This research showed whether it is practical to identify rail indications using image classification and object detection techniques from ultrasonic data and which model performs better among the above-mentioned methods. The methodology includes data processing, labeling, and using different conventional neural networks to develop the model for both image classification and object detection. The results of CNNs for image classification, and YOLOv5 for object detection show 98%, and 99% accuracy respectively. These models can identify rail artifacts efficiently and accurately in real-life scenarios, which can improve automated railway infrastructure monitoring and maintenance.

关键词： Ultrasonic sensors CNNs Artifacts Rail defects image classification Object detection

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XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition

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SIGNAL image AND VIDEO processing 2025年第5期19卷 1-18页

作者： Mohana, M. Subashini, P. Ghinea, George Avinashilingam Inst Ctr Machine Learning & Intelligence Dept Comp Sci Coimbatore India Brunel Univ Dept Comp Sci London England

Facial expression recognition (FER) continues to be a vibrant research field, driven by the increasing need for its practical applications in areas such as e-learning, healthcare, candidate interview analysis, and more. Most deep learning approaches in supervised FER systems heavily rely on large, labeled datasets. Implementing FER in Convolutional Neural Networks (CNNs) often requires many layers, leading to extended training times and difficulties in finding optimal parameters. This can result in challenges in creating distinct facial expression patterns for classification, leading to poor real-time emotion classification In this paper, we propose a novel approach known as the deep Semi-supervised Convolutional Sparse Autoencoder to address the aforementioned issues and enhance FER performance and prediction accuracy. This approach comprises two parts: (i) Initially, a deep convolutional sparse autoencoder is trained with unlabeled samples of facial expressions. Here, sparsity is introduced in the convolutional block to enforce penalties, focusing on more relevant features for feature representation in the latent space. (ii) A trained encoder with a feature map is connected to a fully connected layer with softmax for final fine-tuning with learned weights and labeled facial expression samples in a semi-supervised approach for emotion classification. This approach was experimented with two benchmark datasets, namely CK + and JAFFE, and achieved significant results of 98.98% and 93.10% accuracy, respectively. The results were analyzed using established state-of-the-art techniques. Additionally, eXplainable AI (XAI) methods like Grad-CAM and image-LIME were employed to interpret the performance and prediction outcomes of the DSCSA model.

关键词： Autoencoder Convolutional sparse autoencoder Facial expression recognition Feature representation Unsupervised learning Semi-supervised learning

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deep learning-based methods for detecting defects in cast iron parts and surfaces

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IET image processing 2024年第1期18卷 47-58页

作者： Wang, Pengyu Jing, Peng Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan Peoples R China Sanlian Pump Ind Co Ltd Maanshan Peoples R China Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Peoples R China

The large size tolerance and positional differences of burrs in cast iron blanks make it easy for traditional teaching polishing paths to cause overcutting or undercutting. Rapid and accurate identification of burrs and real-time correction of polishing trajectories are key technical issues for achieving high-precision polishing. Here, a deep learning-based method for defect detection in cast iron parts and surfaces is proposed. Firstly, a self-made dataset of cast iron parts and surface defects is created and annotated, and a variety of data augmentation methods are used to expand the number of samples in the original dataset, alleviating the problem of small sample size. Then, the coordinate attention mechanism is introduced into the backbone network to allocate more attention to the defect target. Finally, the bidirectional weighted feature pyramid network (BiFPN) is used in the feature fusion network to replace the original path aggregation network, improving the model's ability to fuse features of different sizes. Experimental results show that compared with the original model, the mean average precision (mAP) is increased by 3.1%, and the average precision (AP) in defect classification is increased by 7.6%, with an FPS of 112, achieving accurate and efficient real-time detection of cast iron parts and surface defects. First, this article used multiple data augmentation methods to alleviate the problem of small sample size in casting datasets. Second, attention mechanism was introduced. Finally, a novel feature fusion layer structure was adopted to improve the original network model. The experiment shows that compared with the original network model, the improved model proposed here has increased the accuracy of casting surface defect recognition category by 7.6%.image

关键词： convolutional neural nets deburring image fusion learning (artificial intelligence)

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A Fatigue Driving Detection Method based on deep learning and image processing 5

A Fatigue Driving Detection Method based on Deep Learning an...

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5th Annual International Conference on Information System and Artificial Intelligence (ISAI)

作者： Wang, Zhong Shi, Peibei Wu, Chao Hefei Normal Univ Sch Comp Sci & Technol 1688 Lianhua Rd Hefei Anhui Peoples R China Univ Sci & Technol China Anhui Prov Key Lab Big Data Anal & Applicat 96 Jinzhai Rd Hefei Anhui Peoples R China Univ Sci & Technol China Network & Informat Ctr 96 Jinzhai Rd Hefei Anhui Peoples R China

Driving fatigue is one of the important causes of traffic accidents. It is of great significance to study fatigue driving detection algorithms to improve human life and property safety. This paper proposes a fatigue driving detection method based on deep learning and image processing. First, the driver's face image is obtained in real time through the camera and the face image is detected using the MTCNN model. Next the image processing is performed on the face image, including three steps: grayscale processing, binarization processing, and human eye detection. Then we check the legitimacy of the human eye image and calculate the eye closure rate, and finally use the PERCLOS principle to analyze the fatigue state of the driver. The experimental results show that the proposed method has high detection rate and low false alarm rate, and has strong practicality.

关键词： image processing

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FPGA-Based Implementation of real-time Cardiologist-Level Arrhythmia Detection and Classification in Electrocardiograms Using Novel deep learning

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2024年第0期 3662-3683页

作者： Chandrasekaran, Saravanakumar Chandran, Srinivasan Selvam, Immaculate Joy SRM Valliammai Engn Coll ECE Chennai Tamil Nadu India Rajalakshmi Engn Coll EEE Chennai Tamil Nadu India Saveetha Engn Coll ECE Chennai Tamil Nadu India

Cardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine-learning (ML) algorithms have extensive training cycles and require manual feature selection;to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (ViT) and the capsule network (CapsNet) into a hybrid model named ViT-Cap. We conduct necessary preprocessing operations, including noise removal and signal-to-image conversion using short-time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT-BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the ViT module. Comparisons with existing conventional models show that our proposed model outperforms the original ViT and CapsNet in terms of classification accuracy for both binary and multi-class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state-of-the-art methodologies confirms the superiority of our framework. Additionally, we configure a field-programmable gate array (FPGA) to implement the proposed model for real-time ar

关键词： accuracy arrhythmia capsule network deep learning electrocardiogram field-programmable gate array power processing time vision transformer

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deep-learning real-time phase retrieval of imperfect diffraction patterns from X-ray free-electron lasers

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NPJ COMPUTATIONAL MATERIALS 2025年第1期11卷 1-10页

作者： Lee, Sung Yun Cho, Do Hyung Jung, Chulho Sung, Daeho Nam, Daewoong Kim, Sangsoo Song, Changyong POSTECH Dept Phys Pohang South Korea POSTECH Photon Sci Ctr Pohang South Korea Max Planck POSTECH Korea Res Initiat Ctr Ultrafast Sci Quantum Matter Pohang South Korea POSTECH XFEL Beamline Div Pohang Accelerator Lab Pohang South Korea

Machine learning is attracting surging interest across nearly all scientific areas by enabling the analysis of large datasets and the extraction of scientific information from incomplete data. Data-driven science is rapidly growing, especially in X-ray methodologies, where advanced light sources and detection technologies produce vast amounts of data that exceed meticulous human inspection capabilities. Despite the increasing demands, the full application of machine learning has been hindered by the need for data-specific optimizations. In this study, we introduce a new deep-learning-based phase retrieval method for imperfect diffraction data. This method provides robust phase retrieval for simulated data and performs well on partially damaged and noisy single-pulse diffraction data from X-ray free-electron lasers. Moreover, the method significantly reduces data processing time, facilitating real-time image reconstructions that are crucial for high-repetition-rate data acquisition. This approach offers a reliable solution to the phase problem to be widely adopted across various research areas confronting the inverse problem.

关键词： Pulse repetition rate

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deep transfer learning-based computer vision for real-time harvest period classification and impurity detection of Porphyra haitnensis

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AQUACULTURE INTERNATIONAL 2024年第4期32卷 5171-5198页

作者： Gao, Zhenchang Huang, Jinxian Chen, Jiashun Shao, Tianya Ni, Hui Cai, Honghao Jimei Univ Sch Sci Dept Phys Xiamen Fujian Peoples R China Jimei Univ Coll Food & Biol Engn Xiamen Fujian Peoples R China Fujian Prov Key Lab Food Microbiol & Enzyme Engn Xiamen Fujian Peoples R China

Seaweed has attracted great attention as a healthy and nutritious food. Traditional seaweed processing plants mainly rely on manual visual inspection to identify and remove inferior seaweed. Accurate and rapid harvest period classification and impurity detection are key to improving productivity and processing speed in seaweed processing plants. Although many studies on seaweed have been conducted in laboratory environments, currently, the plants lack effective tools to obtain real-time and reliable information on seaweed quality. To address this challenge, the deep transfer learning-based computer vision was applied to identify inferior seaweeds, including those from the third harvest, fourth harvest, and impure seaweeds in this work. Specifically, YOLOv8 and YOLOv5 were utilized as base transfer learning models. By loading various pre-trained weight files, this study was able to automatically classify Porphyra haitnensis into four categories based on the harvest period and simultaneously detect four types of common impurities in it. Among the tested models, YOLOv8n-cls achieved the best trade-off in classifying the harvest period, with a Top-1 accuracy of 93.5%. This represented a significant improvement of 16% compared to the performance without transfer learning. The detection speed for a single image was 8.2 ms, and the model size was only 2.82 Mb. On the other hand, YOLOv8n exhibited outstanding performance in impurity detection, with a mean average precision of 99.14%, a single image detection speed of 4.3 ms, and a model size of 5.95 Mb. The results demonstrated the potential of YOLOv8 with transfer learning to objectively assist or even replace decision-making by assembly line workers. This study will not only enhance the quality control, production efficiency, and economic benefits of the seaweed processing industry but also drive the automation equipment and systems of seaweed-related enterprises towards greater intelligence and efficiency.

关键词： Transfer learning Computer vision YOLOv8 Seaweed Harvest period Impurity detection

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Implementation of a Virtual Assistant System Based on deep Multi-modal Data Integration

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JOURNAL OF SIGNAL processing SYSTEMS FOR SIGNAL image AND VIDEO TECHNOLOGY 2024年第3期96卷 179-189页

作者： Baek, Sungdae Kim, Jonghong Lee, Junwon Lee, Minho Kyungpook Natl Univ Sch Elect & Elect Engn Daegu 41566 South Korea Keimyung Univ Dongsan Med Ctr Dept Res Daegu South Korea Kyungpook Natl Univ Grad Sch Artificial Intelligence Daegu 41566 South Korea

In this study, we propose a virtual assistant system that is applied to real life using signal processing and deep learning. First, the overall structure of the proposed system that integrates and controls various modules is introduced, after which we present a multi-modal fusion module that provides services to users. It integrates a natural language processing module for interpreting Korean chatbots and a behavior recognition module for understanding user behavior using an RGB camera. In addition, a hand gesture recognition module was utilized to understand the user's intentions using depth and RGB images. We explain the implementation of a customized service system with several parts: i) a user interface module that interacts with the user, ii) a face recognition module that distinguishes different users, and iii) a voice processing module that can replace the input and output methods through a keyboard and monitor. To check the performance of each module, a testbed was configured in an office environment. Through test results, we successfully demonstrate the realization of the proposed system in real life Finally, we list the challenges discovered during the operation of this system and suggest directions for further research.

关键词： Integrated system Transfer learning Natural language processing image processing Action recognition Gesture recognition

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DMAeEDNet: Dense Multiplicative Attention Enhanced Encoder Decoder Network for Ultrasound-Based Automated Breast Lesion Segmentation

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IEEE ACCESS 2024年 12卷 60541-60555页

作者： Saini, Manali Afrin, Humayra Sotoudehnia, Setayesh Fatemi, Mostafa Alizad, Azra Mayo Clin Coll Med & Sci Dept Radiol Rochester MN 55905 USA Mayo Clin Coll Med & Sci Dept Physiol & Biomed Engn Rochester MN 55905 USA

Automated and precise segmentation of breast lesions can facilitate early diagnosis of breast cancer. Recent research studies employ deep learning for automatic segmentation of breast lesions using ultrasound imaging. Numerous studies introduce somewhat complex modifications to the well adapted segmentation network, U-Net for improved segmentation, however, at the expense of increased computational time. Towards this aspect, this study presents a low complex deep learning network, i.e., dense multiplicative attention enhanced encoder decoder network, for effective breast lesion segmentation in the ultrasound images. For the first time in this context, two dense multiplicative attention components are utilized in the encoding layer and the output layer of an encoder-decoder network with depthwise separable convolutions, to selectively enhance the relevant features. A rigorous performance evaluation using two public datasets demonstrates that the proposed network achieves dice coefficients of 0.83 and 0.86 respectively with an average segmentation latency of $19 ms$ . Further, a noise robustness study using an in-clinic recorded dataset without pre-processing indicates that the proposed network achieves dice coefficient of 0.72. Exhaustive comparison with some commonly used networks indicate its adeptness with low time and computational complexity demonstrating feasibility in real time.

关键词： Lesions Breast image segmentation Ultrasonic imaging Decoding Feature extraction real-time systems Ultrasound breast lesion segmentation deep learning U-Net convolution neural network

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New Statistic Detector for Structural image Similarity

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IEEE TRANSACTIONS ON SIGNAL processing 2025年 73卷 1168-1183页

作者： Diaw, Moustapha Retraint, Florent Morain-Nicolier, Frederic Delahaies, Agnes Landre, Jerome Univ Reims CReST EA 3804 IUT Troyes F-51097 Reims France Univ Technol Troyes LIST3N UTT F-10300 Troyes France Jonkoping Univ Comp Sci & Informat Dept S-55318 Jonkoping Sweden

Social networks like LinkedIn, Facebook, and Instagram contribute significantly to the rise of image prevalence in daily life, with numerous images posted in everyday. Detecting image similarity is crucial for many applications. While deep learning methods like Learned Perceptual image Patch Similarity (LPIPS) are popular, they often overlook image structure. An alternative method involves using pre-trained models ($e.g.$, LeNet-$5$ and VGG-$16$) to extract features and employing classifiers. However, deep learning methods demand substantial computational resources and they also suffer from uncontrolled false alarms. This paper proposes a novel Generalized Likelihood Ratio Test (GLRT) detector based on a hypothesis testing framework to identify the similarity of structural image pairs. The proposed approach minimizes the need for extensive computational resources, and false alarms can be regulated by employing a threshold. The detector is applied to Local Dissimilarity Maps (LDM), with gray-level values modeled by a statistical distribution. Experimental results on simulated and real data confirm its effectiveness for structural similarity detection. Additionally, a Simple Likelihood Ratio Test (SLRT) is tested on simulated data. Comparisons with deep learning and classical measures like Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM) show the proposed detector performs comparably or better in terms of Area Under the Curve (AUC) with less computing time, especially for structural similarity.

关键词： Feature extraction Detectors Convolutional neural networks Transformers image quality Noise Computational modeling Indexes image classification Training Classification local dissimilarity map weibull distribution GLRT similarity measures pre-trained CNN

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