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real-time Moving Object Detection for 3-D LiDAR Using Occlusion Accumulation in Range image

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kim, Junha Kim, Haram Oh, Changsuk Kim, Changhyeon Kim, H. Jin Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst ASRI Seoul 08826 South Korea 42dot Seoul 13449 South Korea Seoul Natl Univ Artificial Intelligence Inst AIIS Seoul 08826 South Korea Samsung Res Seoul 06765 South Korea Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea

deep learning methods have been applied to detect moving objects from 3-D LiDAR data, but they require extra computing devices, such as graphics processing units (GPUs), and often struggle to operate at real-time LiDAR frame rates. In addition, retraining is necessary for new environments, which demands significant resources. Nonlearning methods that operate online without prior maps and rely solely on a central processing unit (CPU) often fail to achieve real-time performance. Furthermore, they are limited in applicability due to their dependence on specific pose estimation algorithms. To address these issues, we propose a novel occlusion accumulation framework in the range image domain for real-time 3-D LiDAR moving object detection on a CPU. By incorporating compensation strategies that account for LiDAR artifacts and measurement sparsity, the proposed method reduces false positives (FPs) and improves detection accuracy. Our approach is also flexible, integrating seamlessly with different pose estimation algorithms without performance degradation. Extensive experiments on KITTI, Apollo, and CARLA datasets demonstrate that the proposed method achieves competitive results compared to state-of-the-art (SOTA) learning-based methods on KITTI and outperforms them in cross-validation on Apollo and CARLA. Moreover, the proposed method operates four times faster than SOTA nonlearning-based methods while offering higher detection accuracy, making it highly suitable for real-world applications in autonomous driving and robotic navigation. We provide all the source code and datasets used in this article to the public at: https://***/JunhaAgu/Mapless_Moving.

关键词： 3-D LiDAR moving object detection occlusion accumulation occlusion accumulation range image range image range image

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Towards a Holistic Approach for UAV-Based Large-Scale Photovoltaic Inspection: A Review on deep learning and image processing Techniques

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TECHNOLOGIES 2025年第3期13卷 117-117页

作者： Barraz, Zoubir Sebari, Imane El Kadi, Kenza Ait Abdelmoula, Ibtihal Ait IAV Hassan II Sch Geomat & Surveying Engn Photogrammetry Cartog Dept Geospatial Technol Smart Decis Res Unit Rabat 10101 Morocco IRESEN GEP UM6P Green Energy Pk Res Platform BenGuerir 43150 Morocco

This paper provides an in-depth literature review on image processing techniques, focusing on deep learning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations for model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, and multimodal fusion of RGB and thermal data are also discussed. Finally, research gaps and opportunities are analyzed to create a holistic, scalable, and real-time inspection workflow for large-scale installation. This review serves as a reference for researchers and industry professionals to advance UAV-based PV inspection.

关键词： anomaly classification deep learning holistic approach image analysis large-scale photovoltaic installations inspection photovoltaic module segmentation UAV images

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deep Convolution Neural Networks for image-Based Android Malware Classification

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Computers, Materials & Continua 2025年第3期82卷 4093-4116页

作者： Amel Ksibi Mohammed Zakariah Latifah Almuqren Ala Saleh Alluhaidan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadhP.O.Box 11442Saudi Arabia

The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of *** resolve this problem,efficient and flexible malware detection tools are *** work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data ***,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:***,***,***,***,*** network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained *** addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional *** success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.

关键词： Android malware detection deep convolutional neural network(DCNN) image processing CIC-AndMal2017 dataset exploratory data analysis VGG16 model

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real-time Self-Supervised Ultrasound image Enhancement Using Test-time Adaptation for Sophisticated Rotator Cuff Tear Diagnosis

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IEEE SIGNAL processing LETTERS 2025年 32卷 1635-1639页

作者： Lee, Haeyun Lee, Kyungsu Yoon, Jong Pil Kim, Jihun Kim, Jun-Young Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan 31253 South Korea Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju 54896 South Korea Kyungpook Natl Univ Sch Med Dept Orthoped Surg Daegu 41566 South Korea Kangnam Univ Div Elect & Semicond Engn Elect Engn Yongin 16979 South Korea Catholic Univ Daegu Sch Med Dept Orthoped Surg Daegu 42472 South Korea

Medical ultrasound imaging is a key diagnostic tool across various fields, with computer-aided diagnosis systems benefiting from advances in deep learning. However, its lower resolution and artifacts pose challenges, particularly for non-specialists. The simultaneous acquisition of degraded and high-quality images is infeasible, limiting supervised learning approaches. Additionally, self-supervised and zero-shot methods require extensive processing time, conflicting with the real-time demands of ultrasound imaging. Therefore, to address the aforementioned issues, we propose real-time ultrasound image enhancement via a self-supervised learning technique and a test-time adaptation for sophisticated rotational cuff tear diagnosis. The proposed approach learns from other domain image datasets and performs self-supervised learning on an ultrasound image during inference for enhancement. Our approach not only demonstrated superior ultrasound image enhancement performance compared to other state-of-the-art methods but also achieved an 18% improvement in the RCT segmentation performance.

关键词： Ultrasonic imaging image restoration Training real-time systems Self-supervised learning Biomedical imaging image enhancement Superresolution image resolution Medical diagnostic imaging Ultrasound image image enhancement test time adaptation rotator cuff tear

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deep learning-based automatic defect detection of photovoltaic modules in infrared, electroluminescence, and red-green-blue images

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ENERGY CONVERSION AND MANAGEMENT 2025年 332卷

作者： Lai, Yi-Sheng Hsieh, Chien-Chun Liao, Ting-Wei Huang, Chao-Yang Kuo, Chung-Feng Jeffrey Natl Taiwan Univ Sci & Technol Dept Mat Sci & Engn Taipei 10607 Taiwan Ind Technol Res Inst Green Energy & Environm Res Labs Hsinchu 310401 Taiwan

This study presents an automated defect detection system for photovoltaic modules that combines image processing techniques with deep learning models. The system identifies 21 types of defects using three imaging methods: infrared imaging, red-green-blue imaging, and electroluminescence imaging. Infrared imaging, captured using a thermal imager mounted on a drone, detects thermal anomalies such as hotspots and open circuits. red-green-blue imaging identifies surface-level defects, including glass breakage, soiling, and vegetation shading. Electroluminescence imaging, obtained with a charge-coupled device camera in a controlled darkroom environment, reveals internal defects such as microcracks, cell degradation, and busbar corrosion. Crossreferencing results from infrared and red-green-blue images facilitates the identification of defect causes, while electroluminescence imaging confirms internal issues and provides targeted recommendations for improvements. The system development was divided into four main stages. First, electroluminescence images were preprocessed and segmented using median filtering, thresholding, edge detection, and perspective correction techniques. Second, color features in infrared and red-green-blue images were enhanced through color space transformation to improve detection accuracy. Third, image datasets were augmented using rotation, flipping, and generative techniques to ensure model reliability. Finally, deep learning models were trained using transfer learning methods and optimized through cross-validation and hyperparameter tuning to achieve optimal performance for each imaging type. The proposed system demonstrates exceptional accuracy: 99.06% for thermal defect detection in infrared images, 100% for surface defect detection in red-green-blue images, and 99.2% for internal defect detection in electroluminescence images. The average processing time per image is less than 0.1 s, making the system suitable for real-time and large-scale p

关键词： Photovoltaic module Automated defect detection system Infrared image RGB image Electroluminescence image

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DSPCI-MTL: Dynamic split point computing in multi-task learning implementation with collaborative intelligence

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ALEXANDRIA ENGINEERING JOURNAL 2025年 124卷 404-421页

作者： Sahin, M. Faruk Yeganli, S. Faegheh Kiani, Farzad Istanbul Atlas Univ Dept Comp Engn Istanbul Turkiye Fatih Sultan Mehmet Vakif Univ Data Sci Applicat & Res Ctr VEBIM Istanbul Turkiye Univ British Columbia Dept Comp Sci Vancouver BC Canada

deep Neural Networks (DNNs) have become a crucial technology in image processing, renowned for their ability to generate effective feature maps. The integration of DNNs within Internet of Things (IoT) environments, particularly in multi-task robots and swarm systems, has positioned them as vital components in various applications. However, their deployment in IoT devices frequently encounters challenges such as limited hardware capabilities, constrained bandwidth, prolonged data transmission times, and image packet loss due to transmission losses. To address these issues, this paper introduces the Multi-Task learning (MTL) method of Collaborative Intelligence (CI) strategy by dynamically distributing computational tasks among edge devices and cloud. This method addresses the potential performance degradation caused by suboptimal computational splitting points of DNN for multiple tasks (segmentation, classification, depth estimation) and compensates for losses under varying network conditions and data sizes. A key innovation of our methodology is the introduction of a dynamic method to determine split points by computing DNN layers based on real-time bandwidth and data volume. In addition, an Auto Encoder (AE) architecture is implemented in the cloud to reconstruct image data packets lost during transmission based on feature map similarity measurements. Experimental results show that processing all transactions in the cloud with specific operational parameters reduces processing time by 38 % compared to traditional methods, while dynamically selecting the split point results in gains of up to 61 %. Furthermore, the proposed method achieves efficiency by reducing energy consumption by up to 50 % compared to cloud-only processing. It demonstrates robustness under varying network delays and reduces inference time by up to 47.5 % under low-latency conditions. In this regard, the innovative use of an AE for data loss reconstruction also shows significant potential in comp

关键词： Auto encoder Collaborative intelligence deep neural network IoT Multi-task learning Data reconstruction

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Evaluation of Transfer learning for Visual Multiple Target Tracking 1

Evaluation of Transfer Learning for Visual Multiple Target T...

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1st International conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jadhav, Anisha Anil California State University Department of Computer Science Monterey Bay Seaside United States

ISBN: (纸本)9798331518578

deep learning has revolutionized high-level image processing tasks, notably image classification and segmentation, by effectively handling multi-dimensional features in image space. This report investigates the application of transfer learning, utilizing features extracted from a pre-trained VGG deep neural network to visual multiple-target tracking (MTT). Traditional feature detectors like the Lucas-Kanade Tracker and Good Features to Track (GFTT) algorithm are computationally efficient but limited in dynamic, cluttered environments, such as those encountered by unmanned aerial systems (UAS). Replacing GFTT with deep feature detectors from VGG is proposed to assess tracking accuracy and improve computational efficiency. Our experiments reveal that, though promising, deep feature extraction results in lower frame rates and less smooth trajectories than traditional methods. This study highlights the potential and current limitations of integrating deep learning features into real-time visual tracking systems. Plans include optimizing the deep learning model for real-time processing, automating filter selection for feature extraction, and exploring additional pre-trained networks to improve tracking performance and efficiency. © 2025 IEEE.

关键词： image segmentation

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A deep-learning Empowered, real-time processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2025年 33卷 1220-1230页

作者： Xia, Yunjia Chen, Jianan Li, Jinchen Gong, Tingchen Vidal-Rosas, Ernesto E. Loureiro, Rui Cooper, Robert J. Zhao, Hubin Univ Coll London UCL Div Surg & Intervent Sci HUB Intelligent Neuroengn HUBIN CREATe London WC1E 6BT England Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England UCL Dept Med Phys & Biomed Engn DOT HUB London WC1E 6BT England

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

关键词： real-time systems US Department of Transportation Hemodynamics Functional near-infrared spectroscopy Optical imaging Three-dimensional displays Feedback amplifiers Jacobian matrices image reconstruction Electroencephalography Functional near-infrared spectroscopy (fNIRS) diffuse optical tomography (DOT) brain-computer interface (BCI) neurofeedback (NFB) motion artifacts deep learning real-time processing

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Online segmentation of street trees from mobile laser scanning data via deep learning image instance segmentation

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OPTICS AND LASER TECHNOLOGY 2025年 184卷

作者： Li, Qiujie Gao, Junjie Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China

A mobile laser scanning (MLS) system can efficiently collect the vertical structure information of street trees through light detection and ranging (LiDAR) movement. Segmenting street tree instances from street MLS data is the primary step in conducting street tree inventories at the single-tree scale, and existing methods are limited by the high computational complexity of three-dimensional (3D) point cloud processing, resulting in low speed and not supporting real-time applications. This paper proposes an efficient and effective online framework for segmenting street trees from street MLS data via deep learning image instance segmentation. First, the point cloud data collected in real time by LiDAR are stored in a first-in-first-out (FIFO) buffer. When the number of new LiDAR frames reaches the set segmentation step, the point cloud data in the FIFO buffer are converted into a three-channel image using point-pixel mapping. Next, a trained high-performance YOLOv8 segmentation model is used to segment an image instance of the street tree quickly. Then, a point cloud instance of the street tree is accurately segmented through optimization at the pixel and point levels. Finally, the segmentation step is adaptively adjusted on the basis of the street tree image instance segmentation result to improve the online processing efficiency. During the experiment, an MLS system equipped with a UTM-30LX-EW sensor is used to collect one-sided street point cloud data containing 149 street trees and other objects, such as landscape plants, buildings, viaducts, lanes, sidewalks, street lights, traffic signs, trash cans, baffles, fences, cars, motorcycles, and pedestrians. Eight YOLOv8 models with different target image sizes are compared, and the 416 x 320 model is selected to balance segmentation accuracy and speed, with an AP90 of 0.9950 and a segmentation time per image of 16.81 ms. Through pixel- and point-level optimization, the F1 score for online segmentation of street tree

关键词： Online segmentation Street tree Mobile laser scanning image instance segmentation Point cloud instance segmentation Adaptive segmentation step

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Cross-task cognitive workload estimation using eye tracking

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SIGNAL image AND VIDEO processing 2025年第5期19卷

作者： Yang, Lin Wang, Lei Xu, Wenchang Wang, Biao Ren, Hanbin Yang, Aijuan Univ Sci & Technol China Sch Biomed Engn Suzhou Div Life Sci & Med Hefei 230026 Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol CAS Key Lab Biomed Diagnost Suzhou 215163 Peoples R China Suzhou ZhongKe Med Device Ind Dev Co Ltd Suzhou 215163 Peoples R China Jinan Guoke Med Technol Dev Co Ltd Jinan 250001 Peoples R China

Cognitive workload is a key factor in understanding human cognitive performance, especially in scenarios that require intensive information processing. This study introduces an innovative method to estimate cognitive workload using eye-tracking data and proposes a novel deep learning model called BiTCADNet (Bidirectional Temporal Convolutional self-Attention Dense Network). Experiments using the newly created dataset "Cognitive-Eye-Movement" and the publicly available dataset "CL-Drive" show that BiTCADNet significantly outperforms traditional deep learning models in terms of accuracy, precision, recall, and F1 scores are significantly better than traditional machine learning methods. The proposed method provides a more effective way to monitor and evaluate cognitive workload in real-time, opening the way for its applications in various human-computer interaction environments.

关键词： deep-learning Cognitive-workload Eye-tracking Self-attention mechanism

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