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Neural-network based high-speed volumetric dynamic optical coherence tomography

BIOMEDICAL OPTICS EXPRESS 2024年第5期15卷 3216-3239页

作者： Liu, Yusong Abd El-Sadek, Ibrahim Morishita, Rion Makita, Shuichi Mori, Tomoko Furukawa, Atsuko Matsusaka, Satoshi Yasuno, Yoshiaki Univ Tsukuba Computat Opt Grp 1-1-1 Tennodai Tsukuba Ibaraki 3058573 Japan Damietta Univ Fac Sci Dept Phys Dumyat 34517 Egypt Univ Tsukuba Fac Med Clin Res & Reg Innovat Tsukuba Ibaraki 3058575 Japan

We demonstrate deep -learning neural network (NN) -based dynamic optical coherence tomography (DOCT), which generates high -quality logarithmic -intensity -variance (LIV) DOCT images from only four OCT frames. The NN model is trained for tumor spheroid samples using a customized loss function: the weighted mean absolute error. This loss function enables highly accurate LIV image generation. The fidelity of the generated LIV images to the ground truth LIV images generated using 32 OCT frames is examined via subjective image observation and statistical analysis of image -based metrics. Fast volumetric DOCT imaging with an acquisition time of 6.55 s/volume is demonstrated using this NN -based method.

关键词： deep learning image metrics Neural networks Optical coherence tomography real time imaging Signal processing

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Low-Light Phase Retrieval With Implicit Generative Priors

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IEEE TRANSACTIONS ON image processing 2024年 33卷 4728-4737页

作者： Manekar, Raunak Negrini, Elisa Pham, Minh Jacobs, Daniel Srivastava, Jaideep Osher, Stanley J. Miao, Jianwei Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA Univ Calif Los Angeles Calif Nanosyst Inst Dept Phys & Astron Los Angeles CA 90095 USA

Phase retrieval (PR) is fundamentally important in scientific imaging and is crucial for nanoscale techniques like coherent diffractive imaging (CDI). Low radiation dose imaging is essential for applications involving radiation-sensitive samples. However, most PR methods struggle in low-dose scenarios due to high shot noise. Recent advancements in optical data acquisition setups, such as in-situ CDI, have shown promise for low-dose imaging, but they rely on a time series of measurements, making them unsuitable for single-image applications. Similarly, data-driven phase retrieval techniques are not easily adaptable to data-scarce situations. Zero-shot deep learning methods based on pre-trained and implicit generative priors have been effective in various imaging tasks but have shown limited success in PR. In this work, we propose low-dose deep image prior (LoDIP), which combines in-situ CDI with the power of implicit generative priors to address single-image low-dose phase retrieval. Quantitative evaluations demonstrate LoDIP's superior performance in this task and its applicability to real experimental scenarios.

关键词： Imaging Diffraction Noise Lighting image reconstruction Training Optimization deep generative models computational imaging phase retrieval inverse problems low light imaging low photon count zero-shot learning

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Online Offloading and Mobility Awareness of DAG Tasks for Vehicle Edge Computing

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第1期22卷 675-690页

作者： He, Xiao Pang, Shanchen Gui, Haiyuan Zhang, Kuijie Wang, Nuanlai Yu, Shihang China Univ Petr Qingdao Inst Software Coll Comp Sci & Technol Qingdao 266580 Peoples R China Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China

Achieving real-time processing of tasks has become a crucial objective in the Internet of Vehicles (IoV) field. During the online generation of tasks in IoV systems, many dependency tasks arrive randomly within continuous time frames, and it is impossible to predict the number of arriving tasks and the dependencies between sub-tasks. Offloading dependent tasks, which are quantity-intensive and have complex dependencies, to appropriate vehicle edge servers (VESs) for online processing of large-scale tasks remains a challenge. Firstly, we innovatively propose a VES task parallel processing framework incorporating a multi-level feedback queue to enhance the cross-slot parallel processing capabilities of the IoV system. Secondly, to reduce the complexity of problem-solving, we employ the Lyapunov optimization method to decouple the online task offloading control problem into single-stage mixed-integer nonlinear programming problem. Finally, we design an online task decision-making algorithm based on multi-agent reinforcement learning to achieve real-time task offloading decisions in complex dynamic IoV environments. To validate our algorithm's superiority in dynamic IoV systems, we compare it with other online task offloading decision-making algorithms. Simulation results show that ours significantly reduces the all-task processing latency of IoV system by 15% compared to the comparison algorithms, and the task average latency time is reduced by 14%.

关键词： Vehicle dynamics Heuristic algorithms Servers Resource management real-time systems Dynamic scheduling Job shop scheduling Energy consumption Industries Delays Internet of Vehicle edge computing directed acyclic graph Lyapunov optimization deep reinforcement learning

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deep learning-Based Defects Detection in Keyhole TIG Welding with Enhanced Vision

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MATERIALS 2024年第15期17卷 3871页

作者： Zhang, Xuan Zhao, Shengbin Wang, Mingdi Soochow Univ Sch Mech & Elect Engn Suzhou 215137 Peoples R China

Keyhole tungsten inert gas (keyhole TIG) welding is renowned for its advanced efficiency, necessitating a real-time defect detection method that integrates deep learning and enhanced vision techniques. This study employs a multi-layer deep neural network trained on an extensive welding image dataset. Neural networks can capture complex nonlinear relationships through multi-layer transformations without manual feature selection. Conversely, the nonlinear modeling ability of support vector machines (SVM) is limited by manually selected kernel functions and parameters, resulting in poor performance for recognizing burn-through and good welds images. SVMs handle only lower-level features such as porosity and excel only in detecting simple edges and shapes. However, neural networks excel in processing deep feature maps of "molten pools" and can encode deep defects that are often confused in keyhole TIG. Applying a four-class classification task to weld pool images, the neural network adeptly distinguishes various weld states, including good welds, burn-through, partial penetration, and undercut. Experimental results demonstrate high accuracy and real-time performance. A comprehensive dataset, prepared through meticulous preprocessing and augmentation, ensures reliable results. This method provides an effective solution for quality control and defect prevention in keyhole TIG welding process.

关键词： keyhole TIG welding deep learning defects detection

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High-speed system-on-chip-based platform for real-time crop disease and pest detection using deep learning techniques

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Mallick, Tausif Murty, D. Omkar Pal, Ranita Mandal, Swagata Saha, Himadri Nath Chakrabarti, Amlan Univ Calcutta AKCSIT Kolkata India Neotia Univ Dept Comp Sci & Engn AI & ML SST Sarisha India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Jalpaiguri Govt Engn Coll Dept Elect & Commun Kolkata India Univ Calcutta Dept Comp Sci SNEC Kolkata India

Crop diseases significantly threaten global agricultural productivity and food security, leading to economic losses and increased pesticide use, which pollutes soil and water and disrupts ecological balance. Mustard and mung bean crops are particularly affected by various diseases and pests such as Alternaria blight, aphids, charcoal rot, bruchids, and mosaic. timely and accurately identifying these diseases and pests are crucial for effective crop management. This research tackles disease classification in mustard and mung bean crops by employing transfer learning, a MobileNetV3based CNN model, and a System-on-Chip (SoC) computing platform. The processing system and processing logic of SoC enhance computing flexibility. Xilinx deep learning Processor Unit (DPU) intellectual property (IP) accelerates disease classification 24 times compared to software counterparts. At the same time, our proposed design enhances the throughput by around 29% and reduces the power consumption by around 19%. MobileNetV3 achieves classification accuracies of 96.14% on mung bean and 93.25% on mustard datasets, surpassing other state-of-the-art methods. A vital aspect of this research is developing a user-friendly mobile application for image capture, communication with SoC, and result display, making disease and pest detection more convenient and accessible. The SoC-based system is versatile and can be extended to classify various crop varieties beyond mung bean and mustard without hardware modifications.

关键词： Plant disease detection Plant pest detection CNN Transfer learning image classification System on chip

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Rcf-yolo: an underwater object detection algorithm based on improved YOLOv10n

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JOURNAL OF real-time image processing 2025年第2期22卷 1-18页

作者： Liang, Xiuman Zhang, Teng Yu, Haifeng Liu, Zhendong North China Univ Sci & Technol Sch Elect Engn Tangshan 063000 Peoples R China

Object detection is a key technology for marine exploration. The detection effect is not ideal because of factors such as the biodiversity and overlapping shadows in the underwater environment. Therefore, a new underwater object detection algorithm called RCF-YOLO is proposed. First, a coordinate enhancement (CE) attention module is designed. Depth-separable convolutions are used to extract the location information of the channel and combine it with spatial information to improve the model's ability to infer global features. Second, we have redesigned the neck with the BiFPN concept, which enhances feature interaction capabilities and optimizes the inference structure. The convolutional operation in the neck path is improved to enhance cross-scale connections, effectively integrating shallow and deep features, achieving a good balance between efficiency and accuracy. Finally, the receptive field convolution (RFAConv) is introduced to solve the parameter sharing problem in complex convolution processing, making the model more flexible in adjusting the convolution kernel weights and more effectively capturing the information in the image. The proposed model was compared with several sets of experiments on the URPC, DUO, and ROUD datasets. With a decrease in both the number of parameters and the complexity of the calculation, the accuracy reached 85.3%, 87.9%, and 84.9%. The experimental results show that the RCF-YOLO model has excellent performance in the underwater detection task.

关键词： deep learning Underwater object detection Attention mechanism YOLOv10n

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EFFECTIVE FEATURE I NFORMATION IDENTIFICATION OF SUGARCANE BASED ON HYBRID deep learning MODELS

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APPLIED ENGINEERING IN AGRICULTURE 2024年第3期40卷 243-257页

作者： Pan, Mingzhang Gou, Xuanyuan Zeng, Yue Wang, Zongrun Yuan, Leyi Liang, Ke Guangxi Univ State Key Lab Conservat & Utilizat Biol Resources Nanning Guangxi Peoples R China Guangxi Univ Nanning Guangxi Peoples R China

The efficiency of intelligent sugarcane harvesters in harvesting depends on the effectiveness of identifying and locating the sugarcane during the harvesting process. In the actual harvesting process, accurately extracting valid features of sugarcane amidst the dense and interwoven sugarcane becomes a challenging task. To address this issue, we propose a hybrid deep learning approach to extract sugarcane stem contours and internal stem node feature information from sugarcane efficiently in the context of a complex harvest. Firstly, this study combined the MobileNetV3 and U-Net networks to segment overall images that contain information about the external contours of the sugarcane stem. Then, the extracted overall profile images were optimized using a variety of image processing techniques to meet the requirements of harvesting. Lastly, the improved YOLOX model was utilized to identify the internal stem node features of sugarcane from the optimized overall images. The experimental results on a real sugarcane dataset show that the proposed external sugarcane stem segmentation model achieves a high mean intersection over union (MIoU) of 91.68% with an average segmentation time of just 0.025 seconds. Moreover, the proposed model for internal stem node recognition in sugarcane achieves an average precision (AP) of 96.19% with an average detection time of 0.026 seconds. Additionally, this study compares image segmentation models such as PSPNet and deepLabv3+ with target detection models such as YoloV5 and YoloV7. The experimental results show that the sugarcane feature extraction models proposed in this article all exhibit high accuracy and robustness.

关键词： Keywords. deep learning image segmentation Machine vision Sugarcane identification Target detection.

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Fast Monocular Measurement via deep learning-Based Object Detection for real-time Gas-Insulated Transmission Line Deformation Monitoring

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ENERGIES 2025年第8期18卷

作者： Yang, Guiyun Yang, Wengang Li, Entuo Wang, Qinglong Han, Huilong Sun, Jie Wang, Meng North China Elect Power Univ Dept Mech Engn Baoding 071003 Peoples R China North China Elect Power Univ Hebei Engn Res Ctr Adv Mfg & Intelligent Operat & Baoding 071003 Peoples R China North China Elect Power Univ Hebei Key Lab Elect Machinery Hlth Maintenance & F Baoding 071003 Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China

Deformation monitoring of Gas-Insulated Transmission Lines (GILs) is critical for the early detection of structural issues and for ensuring safe power transmission. In this study, we introduce a rapid monocular measurement method that leverages deep learning for real-time monitoring. A YOLOv10 model is developed for automatically identifying regions of interest (ROIs) that may exhibit deformations. Within these ROIs, grayscale data is used to dynamically set thresholds for FAST corner detection, while the Shi-Tomasi algorithm filters redundant corners to extract unique feature points for precise tracking. Subsequent subpixel refinement further enhances measurement accuracy. To correct image tilt, ArUco markers are employed for geometric correction and to compute a scaling factor based on their known edge lengths, thereby reducing errors caused by non-perpendicular camera angles. Simulated experiments validate our approach, demonstrating that combining refined ArUco marker coordinates with manually annotated features significantly improves detection accuracy. Our method achieves a mean absolute error of no more than 1.337 mm and a processing speed of approximately 0.024 s per frame, meeting the precision and efficiency requirements for GIL deformation monitoring. This integrated approach offers a robust solution for long-term, real-time monitoring of GIL deformations, with promising potential for practical applications in power transmission systems.

关键词： gas-insulated transmission lines (GILs) deep learning YOLO monocular measurement

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BSMO: Enhancing Multitask learning Through Batch Swapping Optimization

BSMO: Enhancing Multitask Learning Through Batch Swapping Op...

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Conference on real-time image processing and deep learning

作者： Spencer, Zephaniah Schirner, Gunar Tabkhi, Hamed UNC Charlotte 9201 Univ City Blvd Charlotte NC 28223 USA Northeastern Univ 360 Huntington Ave Boston MA 02115 USA

ISBN: (纸本)9781510673878;9781510673861

With the advent of deep learning, there has been an ever-growing list of applications to which deep Convolutional Neural Networks (DCNNs) can be applied. The field of Multi-Task learning (MTL) attempts to provide optimizations to many-task systems, improving performance by optimization algorithms and structural changes to these networks. However, we have found that current MTL optimization algorithms often impose burdensome computation overheads, require meticulously labeled datasets, and do not adapt to tasks with significantly different loss distributions. We propose a new MTL optimization algorithm: Batch Swapping with Multiple Optimizers (BSMO). We utilize single-task labeled data to train on a multi-task hard parameter sharing (HPS) network through swapping tasks at the batch level. This dramatically increases the flexibility and scalability of training on an HPS network by allowing for per-task datasets and augmentation pipelines. We demonstrate the efficacy of BSMO versus current SO TA algorithms by benchmarking across contemporary benchmarks & networks.

关键词： deep learning Multi-Task learning real-time Vision

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Identification and characteristic analysis of internal defects in rock-filled concrete based on deep learning method

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MATERIALS TODAY COMMUNICATIONS 2024年 41卷

作者： Ren, Tao Zhang, Yufan Yang, Tao Chen, Qiaolin Yu, Lei Wang, Guoji Song, Meizhou Guizhou Univ Coll Civil Engn Guiyang 550025 Peoples R China Guiyang Shougang Real Estate Dev Co Guiyang 550025 Peoples R China

The distribution characteristics and geometric morphology characteristics of defects within RFC are important factors affecting the strength properties and rupture morphology of RFC. However, the excessive size of commonly used aggregates for RFC leads to difficulties in conducting in-depth experimental studies indoors. Based on the improved U-Net and image processing technology, this research establishes an integrated model for the identification, classification, and extraction of defects inside the RFC, quantitatively counts and analyzes the acquired defect distribution characteristics and geometrical morphology characteristics, and establishes a defect characteristic distribution function that can be used for the numerical reconstruction of defects. In order to realize the acceleration of U-Net training using training weights, use VGG-16 with the fully connected layer removed instead of the Encoder part of the U-Net. The integrated model in this research can realize automatic identification, classification, and extraction of multiple types of defects at the same time, and the established distribution function of defect characteristics provides a data basis and new ideas for the establishment of RFC three-dimensional numerical models containing real defects.

关键词： deep learning improved U -Net image processing technology defect characteristic function rock-filled concrete

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