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Acoustic-to-hyper-spectral: real-time perimeter intrusion detection system monitoring through learnable filters and hyper-spectral image generation from distributed acoustic sensing systems

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OPTICS EXPRESS 2025年第3期33卷 4109-4126页

作者： Pierau, Ruth-emely Katsifolis, Jim Meehan, Alaster Rezatofighi, Hamid Stuckey, Peter J. Monash Univ Clayton Vic 3800 Australia Univ Melbourne ARC Training Ctr Optimisat Technol Integrated Meth Parkville Vic 3052 Australia AVA Risk Grp Co Future Fibre Technol 10 Hartnett CL Mulgrave Vic 3170 Australia

This paper presents an integrated distributed acoustic sensing (DAS) system with artificial intelligence to provide real-time system monitoring for fence perimeter and buried system applications. The DAS system is a Rayleigh backscatter based fibre optic sensing system that has been deployed in two real-world, commercial applications to detect acoustic wave propagation and scattering along perimeter lines, and classify intrusions accurately. What we believe to be three novel signal processing methods are proposed to train filters for automatically selecting frequency bands from the power spectrum and generating hyper-spectral images from the data gathered by the DAS system without expert knowledge. The hyper-spectral images are analyzed by a neural network based object detection model. The system achieves 81.8% accuracy on a fence perimeter installation and 60.4% accuracy on a buried system application in detecting and classifying various intrusion events. The evaluation interval of the integrated DAS system framework between event sensing and detection does not exceed 5 s. (c) 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

关键词： deep learning Hyperspectral imaging Machine learning Neural networks Signal processing Wave propagation

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FastBeltNet: a dual-branch light-weight network for real-time conveyor belt edge detection

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JOURNAL OF real-time image processing 2024年第4期21卷 123-123页

作者： Zhao, Xing Zeng, Minhao Dong, Yanglin Rao, Gang Huang, Xianshan Mo, Xutao Anhui Univ Technol Sch Microelect & Data Sci Maanshan 243032 Anhui Peoples R China Shanghai Meishan Iron & Steel Co Ltd Steelmaking Plant Nanjing 210039 Jiangsu Peoples R China Anhui Univ Technol Sch Innovat & Entrepreneurship Maanshan 243032 Anhui Peoples R China

Belt conveyors are widely used in multiple industries, including coal, steel, port, power, metallurgy, and chemical, etc. One major challenge faced by these industries is belt deviation, which can negatively impact production efficiency and safety. Despite previous research on improving belt edge detection accuracy, there is still a need to prioritize system efficiency and light-weight models for practical industrial applications. To meet this need, a new semantic segmentation network called FastBeltNet has been developed specifically for real-time and highly accurate conveyor belt edge line segmentation while maintaining a light-weight design. This network uses a dual-branch structure that combines a shallow spatial branch for extracting high-resolution spatial information with a context branch for deep contextual semantic information. It also incorporates the Ghost blocks, Downsample blocks, and Input Injection blocks to reduce computational load, increase processing frame rate, and enhance feature representation. Experimental results have shown that FastBeltNet has performed comparatively better than some existing methods in different real-world production settings, achieving promising performance metrics. Specifically, FastBeltNet achieves 80.49% mIoU accuracy, 99.89 FPS processing speed, 895 k parameters, 8.23 GFLOPs, and 430.95 MB peak CUDA memory use, effectively balancing accuracy and speed for industrial production.

关键词： Belt deviation Machine vision deep learning Edge detection

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A vision system based on CNN-LSTM for robotic citrus sorting Author links open overlay panel

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Information processing in Agriculture 2024年第1期11卷 14-25页

作者： Yonghua Yu Xiaosong An Jiahao Lin Shanjun Li Yaohui Chen College of Engineering Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River Ministry of Agriculture and Rural AffairsWuhan 430070China National R&D Center for Citrus Preservation Wuhan 430070China Citrus Mechanization Research Base Ministry of Agriculture and Rural AffairsWuhan 430070China

Compared with manual sorting of citrus fruit,vision-based sorting solutions can help achieve higher accuracy and *** this study,we present a vision system based on CNN-LSTM,which can cooperate with robotic grippers for real-time sorting and is readily applicable to various citrus processing plants.A CNN-based detector was adopted to detect the defective oranges in view and temporarily classify them into corresponding types,and an LSTM-based predictor was used to predict the position of the oranges in a future frame based on image sequential *** fusion of CNN and LSTM networks enabled the system to track defective ones during rotation and identify their true types,and their future path was also predicted which is vital for predictive control of visually guided robotic *** detection accuracy of 94.1%was obtained based on experimental results,and the error for path prediction was within 4.33 pixels 40 frames *** average time to process a frame was between 28 and 62 frames per second,which also satisfied real-time *** results proved the potential of the proposed system for automated citrus sorting with good precision and efficiency,and it can be readily extended to other fruit crops featuring high versatility.

关键词： deep learning Long short-term memory Vision system Online citrus sorting Path prediction

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Coarse-to-fine blind image deblurring based on K-means clustering

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VISUAL COMPUTER 2024年第1期40卷 333-344页

作者： Eqtedaei, Amir Ahmadyfard, Alireza Shahrood Univ Technol Fac Elect Engn Shahrud Iran

Blind image deblurring is a challenging image processing problem, and a proper solution for this problem has many applications in the real world. This is an ill-posed problem, as both the sharp image and blur kernel are unknown. The traditional methods based on maximum a posterior (MAP) apply heavy constraints on the latent image or blur kernel to find the solution. However, these constraints are not always effective;meanwhile, they are very time-consuming. Recently, new approaches based on deep learning have emerged. The methods based on this approach suffer from two problems: the need for a large number of images and kernels for training and also the dependency of the result on the training data. In this paper, we propose a multiscale method based on MAP framework for image motion deblurring. In this method, we represent the blurry image in different scales. We suggest segmenting the image of each scale using kappa-means clustering. Using the image information at dominant edges guided by the segmented images, the blur kernel is estimated at each scale. The blur kernel at the finest level of the pyramid is estimated from the coarser levels in a coarse-to-fine manner. Unlike the existing MAP-based methods, the proposed method does not need mathematically complicated assumptions to estimate the intermediate latent image. So the proposed image deblurring is run fast. We evaluated the proposed method and compared it to the existing methods. The experimental results on real and synthetic blurry images demonstrate that the proposed scheme has promising results. The proposed method competes with the existing MAP-based methods for reconstructing qualitative sharp images, while the execution time for our method is considerably less.

关键词： Blind image deblurring ?-means clustering Kernel estimation

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learning-Based Safe Control for Robot and Autonomous Vehicle Using Efficient Safety Certificate

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 551-564页

作者： Nakshatri, Hemanth S. Prakash, Jaya Indian Inst Sci Dept Instrumentat & Appl Phys Bengaluru 560012 India

Frequency domain photoacoustic (FDPA) imaging has great potential in a clinical setting compared to time-domain photoacoustic imaging due to its reduced cost and small form factors. However FDPA system struggles with lower signal to noise ratio, necessitating the need for advanced image reconstruction methods. Most of the image reconstruction approaches in FDPA imaging are based on analytical or model based schemes. Very less emphasis has been placed on developing deep learning based approaches for FDPA imaging. In this work, a image translation network was developed with the ability to directly map from sinogram data (complex-valued) to the initial pressure rise distribution (real-valued) for FD-PA imaging. This architecture was based on a Long Short Term Memory (LSTM) backbone (with adjoined real and imaginary parts of the complex sinogram data as input) followed by a fully connected layer, which is then passed through a convolution and transposed convolution layer pair. The result of the FDPA-LSTM architecture was compared with direct translational networks based on ResNet, UNet and AUTOMAP and found to have an improvement of about 15% in terms of PSNR and 10% in terms of SSIM with 150 degrees data acquisition limited-view angle. Further, the FDPA-LSTM was also compared with post-processing UNet architecture on backprojection and Tikhonov regularized reconstruction. A 20% improvement in terms of PSNR with backprojection and post-processing UNet was observed. Further FDPA-LSTM had similar performance as Tikhonov and post-processing UNet (with 75 times acceleration). The developed scheme will indeed be very useful for achieving accelerated and accurate frequency domain photoacoustic imaging.

关键词： Frequency-domain analysis image reconstruction Logic gates Frequency modulation deep learning time-domain analysis Matrix converters frequency domain LSTM neural network photoacoustic imaging

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An atrial fibrillation signals analysis algorithm in line with clinical diagnostic criteria

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SIGNAL processing 2025年 236卷

作者： Li, Yongjian Chen, Meng Qu, Xinliang Han, Baokun Liu, Lei Wei, Shoushui Shandong Univ Sch Control Sci & Engn Jinan Peoples R China Shandong First Med Univ Affiliated Hosp 1 Jinan Peoples R China

The detection of atrial fibrillation using deep learning techniques is a hot topic in the field of signal processing. However, simply stacking modules to pursue accuracy, or compressing inputs and parameters to pursue real-time performance, leads to gambling problem between information redundancy and information loss in deep learning algorithms. At the same time, the features obtained by deep learning lack interpretability. Therefore, this study proposes a T neural network (T-Net) that integrates feature extraction, selection, and fusion. In T-Net, horizontal path extracts multi-scale information of electrocardiograms through multi-level feature reuse, feature filter embeds attention mechanism and voting algorithm internally to select information flow, and vertical path uses channel-wise point-to-point weighting to capture the nonlinear relationships of multi-scale information. Through pre-training and fine-tuning on the MIT-BIH atrial fibrillation database consisting of 23 patients, and testing on the Shandong Provincial Hospital database consisting of 252 patients, T-Net achieved accuracy, specificity, sensitivity, and F1 score of 97.95 %, 97.01 %, 98.89 %, and 97.97 %, respectively. T-Net addresses the gambling problem between information redundancy and information insufficiency, and the extracted features demonstrate good interpretability consistent with clinical diagnostic criteria, showing promising clinical application prospects.

关键词： Interpretability deep learning Atrial fibrillation Information redundancy Information loss

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learning Scene Representations for Human-assistive Displays Using Self-attention Networks

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ACM TRANSACTIONS ON MULtimeDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年第7期20卷 1–26页

作者： Ruiz-Serra, Jaime White, Jack Petrie, Stephen Kameneva, Tatiana Mccarthy, Chris Swinburne Univ Technol Hawthorn Vic Australia Univ Sydney Darlington Durham England Univ Melbourne Parkville Vic Australia

Video-see-through (VST) augmented reality (AR) is widely used to present novel augmentative visual experiences by processing video frames for viewers. Among VST AR systems, assistive vision displays aim to compensate for low vision or blindness, presenting enhanced visual information to support activities of daily living for the vision impaired/deprived. Despite progress, current assistive displays suffer from a visual information bottleneck, limiting their functional outcomes compared to healthy vision. This motivates the exploration of methods to selectively enhance and augment salient visual information. Traditionally, vision processing pipelines for assistive displays rely on hand-crafted, single-modality filters, lacking adaptability to time-varying and environment-dependent needs. This article proposes the use of deep Reinforcement learning (DRL) and Self-attention (SA) networks as a means to learn vision processing pipelines for assistive displays. SA networks selectively attend to task-relevant features, offering a more parameter-and compute-efficient approach to RL-based task learning. We assess the feasibility of using SA networks in a simulation-trained model to generate relevant representations of real-world states for navigation with prosthetic vision displays. We explore two prosthetic vision applications, vision-to-auditory encoding, and retinal prostheses, using simulated phosphene visualisations. This article introduces SA-px, a general-purpose vision processing pipeline using self-attention networks, and SA-phos, a display-specific formulation targeting low-resolution assistive displays. We present novel scene visualisations derived from SA image patches importance rankings to support mobility with prosthetic vision devices. To the best of our knowledge, this is the first application of self-attention networks to the task of learning vision processing pipelines for prosthetic vision or assistive displays.

关键词： Vision processing deep reinforcement learning prosthetic vision display algorithms

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Mamba-Based VoxelMorph Framework for Cardiovascular Disease Imaging and Risk Assessment

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IEEE ACCESS 2025年 13卷 78120-78137页

作者： Jabbar, Muhammad Kashif Huang, Jianjun Jabbar, Ayesha Rehman, Zaka Ur Shenzhen Univ Coll Elect & Informat Engn Shenzhen 518060 Peoples R China Guangdong Prov Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Multimedia Univ Fac Engn Cyberjaya 63100 Malaysia

Cardiovascular diseases (CVDs), particularly coronary artery disease (CAD), remain the leading cause of global mortality, necessitating advanced diagnostic solutions. Accurate deformable image registration plays a crucial role in enhancing segmentation precision and classification performance in cardiovascular imaging. However, existing registration methods, including VoxelMorph, face limitations in computational efficiency and memory usage, restricting their real-time applicability for high-resolution cardiac imaging. This study proposes the Mamba-Optimized VoxelMorph framework, which leverages GPU-based parallelization and memory optimization to address these challenges. The framework achieves superior registration accuracy, yielding a Dice Similarity Coefficient (DSC) of 0.95 and Normalized Cross-Correlation (NCC) of 0.90, while reducing computational time by 40% and memory usage to 800 MB. These advancements ensure efficient alignment of complex cardiac structures, thereby improving segmentation accuracy and classification reliability. By addressing these critical limitations, the Mamba-Optimized VoxelMorph framework significantly enhances cardiovascular imaging, enabling precise, scalable, and real-time deformable image registration for improved CAD diagnosis and treatment planning.

关键词： Imaging Accuracy real-time systems image registration Heart deep learning Cardiovascular diseases Arteries Memory management image resolution Deformable image registration mamba optimization cardiovascular disease (CVD) imaging component analysis VoxelMorph framework real-time cardiac diagnostics

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Development of Safety Check Support Device for Drivers Using Side Cameras on Rolling Stock

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Quarterly Report of RTRI (Railway Technical Research Institute) 2024年第3期65卷 176-181页

作者： Goda, Wataru Mukojima, Hiroki Nagamine, Nozomi Image Analysis Laboratory Information and Communication Technology Division

This paper presents the development of a safety check support device aimed at further enhancing platform safety checks using side cameras installed on rolling stock. The device employs deep learning-based image processing techniques to detect passenger approach on platforms in real-time from camera footage, notifying the driver to assist with safety checks. Additionally, the device is equipped with automatic passenger counting functionality at each station. An overview of the developed device and the results of evaluation experiments are provided. © 2024 Ken-yusha Inc.. All rights reserved.

关键词： image enhancement

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image-based characterization of laser scribing quality using transfer learning

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第5期34卷 2307-2319页

作者： Bisheh, Mohammad Najjartabar Wang, Xinya Chang, Shing, I Lei, Shuting Ma, Jianfeng Kansas State Univ Dept Ind & Manufactruing Syst Engn 2061 Rathbone Hall1701B Platt St Manhattan KS 66506 USA St Louis Univ Dept Aerosp & Mech Engn 3450 Lindell Blvd St Louis MO 63103 USA

Ultrafast laser scribing provides a new microscale materials processing capability. Due to the processing speed and high-quality requirement in modern industrial applications, it is important to measure and monitor quality characteristics in real time during a scribing process. Although deep learning models have been successfully applied for quality monitoring of laser welding and laser based additive manufacturing, these models require a large sample for training and a time-consuming data labelling procedure for a new application such as the laser scribing process. This paper presents a study on image-based characterization of laser scribing quality using a deep transfer learning model for several quality characteristics such as debris, scribe width, and straightness of a scribe line. images taken from the laser scribes on intrinsic Si wafers are examined. These images are labelled in a large and a small dataset, respectively. The large dataset includes 154 and small dataset includes 21 images. A novel transfer deep convolutional neural network (TDCNN) model is proposed to learn and assess scribe quality using the small dataset. The proposed TDCNN is able to overcome the data challenge by leveraging a convolutional neural network (CNN) model already trained for basic geometric features. Appropriate image processing techniques are provided to measure scribe width and line straightness as well as total scribe and debris area using classified images with 96 percent accuracy. Validating model's performance based on the small data set, the model trained with the large dataset has a similar accuracy of 97 percent. The trained TDCNN model was also applied to a different scribing application. With 10 additional images to retrain the model, the model accuracy performs as well as the original model at 96 percent. Based on the proposed TDCNN classification of debris on a scribed image of straight lines, two algorithms are proposed to compute scribe width and straightness. The

关键词： Laser scribing image processing Transfer learning Process monitoring

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