Relevant bibliographies by topics / Lines detection and segmentation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Lines detection and segmentation'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Lines detection and segmentation"

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Wang, Shengli, Zhangpeng Zhou, and Wenbin Zhao. "Semantic Segmentation and Defect Detection of Aerial Insulators of Transmission Lines." Journal of Physics: Conference Series 2185, no. 1 (2022): 012086. http://dx.doi.org/10.1088/1742-6596/2185/1/012086.

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Abstract Aiming at the problems of low accuracy and poor generalization ability of insulator defect detection in complex aerial images by existing insulator defect detection algorithms, the possibility of using semantic segmentation technology to simplify insulator features in complex images is explored. The semantic segmentation model DeepLabv3 is cascaded with the target detector yolov3 to realize the semantic segmentation of insulators in aerial images and the detection of defects. The experimental results show that the use of the strategy of semantic segmentation and target detection can i
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Chen, Mo, Sheng Cheng, Yan Liu, Qifan Yin, and Hongfu Zuo. "A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines." Applied Sciences 15, no. 10 (2025): 5342. https://doi.org/10.3390/app15105342.

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Checking the distance between aircraft fire detection lines is a crucial task in the conformity inspection process of civil aircraft manufacturing. Currently, this task is mainly performed manually, which is inefficient and prone to errors and omissions. To address this issue, we propose a method for detecting the distance between aircraft fire detection lines based on the Segment Anything Model (SAM). In this method, we develop a general model for aircraft parts image segmentation and detection, named the Aircraft Segment Anything Model (ASAM). This model uses a low-rank fine-tuning strategy
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Tao, Zhen, Shiwei Ren, Yueting Shi, Xiaohua Wang, and Weijiang Wang. "Accurate and Lightweight RailNet for Real-Time Rail Line Detection." Electronics 10, no. 16 (2021): 2038. http://dx.doi.org/10.3390/electronics10162038.

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Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, whi
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Li, Aohua, Dacheng Li, and Anjing Wang. "A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines." Sensors 25, no. 9 (2025): 2903. https://doi.org/10.3390/s25092903.

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Transmission-line defect detection is crucial for grid operation. Existing methods struggle to balance defect localization and fine segmentation. Therefore, this study proposes a novel cascaded two-stage framework that first utilizes YOLOv5s for the global localization of defective regions, and then uses U-Net for the fine segmentation of candidate regions. To improve the segmentation performance, U-Net adopts a transfer learning strategy based on the VGG16 pretrained model to alleviate the impact of limited dataset size on the training effect. Meanwhile, a hybrid loss function that combines D
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Song, Xiang, Xiaoyu Che, Huilin Jiang, et al. "A Robust Detection Method for Multilane Lines in Complex Traffic Scenes." Mathematical Problems in Engineering 2022 (March 8, 2022): 1–14. http://dx.doi.org/10.1155/2022/7919875.

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The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed in
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Abbasi, Soolmaz, Assefa Seyoum Wahd, Shrimanti Ghosh, et al. "Improved A-Line and B-Line Detection in Lung Ultrasound Using Deep Learning with Boundary-Aware Dice Loss." Bioengineering 12, no. 3 (2025): 311. https://doi.org/10.3390/bioengineering12030311.

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Lung ultrasound (LUS) is a non-invasive bedside imaging technique for diagnosing pulmonary conditions, especially in critical care settings. A-lines and B-lines are important features in LUS images that help to assess lung health and identify changes in lung tissue. However, accurately detecting and segmenting these lines remains challenging, due to their subtle blurred boundaries. To address this, we propose TransBound-UNet, a novel segmentation model that integrates a transformer-based encoder with boundary-aware Dice loss to enhance medical image segmentation. This loss function incorporate
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Yan, Jichen, Xiaoguang Zhang, Siyang Shen, et al. "A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs." Drones 7, no. 9 (2023): 574. http://dx.doi.org/10.3390/drones7090574.

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Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which
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Xing, Junyao, Xiaojun Bi, and Yu Weng. "A Multi-Scale Hybrid Attention Network for Sentence Segmentation Line Detection in Dongba Scripture." Mathematics 11, no. 15 (2023): 3392. http://dx.doi.org/10.3390/math11153392.

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Dongba scripture sentence segmentation is an important and basic work in the digitization and machine translation of Dongba scripture. Dongba scripture sentence segmentation line detection (DS-SSLD) as a core technology of Dongba scripture sentence segmentation is a challenging task due to its own distinctiveness, such as high inherent noise interference and nonstandard sentence segmentation lines. Recently, projection-based methods have been adopted. However, these methods are difficult when dealing with the following two problems. The first is the noisy problem, where a large number of noise
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Chen, Yong, Yun-hui Wang, Song Li, and Meng Li. "Transmission Line Instance Segmentation Algorithm Based on YOLACT." Journal of Physics: Conference Series 2562, no. 1 (2023): 012018. http://dx.doi.org/10.1088/1742-6596/2562/1/012018.

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Abstract In the field of intelligent power patrol inspection, the transmission line is an important identification and detection target. The measurement of line spacing and ground distance are key technologies in the field of inspection. Therefore, it is necessary to quickly and accurately segment transmission lines. The transmission lines occupy a large span and vary widely in length. To improve the segmentation rate and accuracy of the transmission lines, we adopted EfficientNet as the main network. With the same accuracy, the number of parameters is reduced by 80% compared with ResNet 101.
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Zhu, Yuhang, Zhezhuang Xu, Ye Lin, Dan Chen, Zhijie Ai, and Hongchuan Zhang. "A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation." Sensors 24, no. 5 (2024): 1635. http://dx.doi.org/10.3390/s24051635.

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Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently e
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Dissertations / Theses on the topic "Lines detection and segmentation"

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Li, Yaqian. "Image segmentation and stereo vision matching based on declivity line : application for vehicle detection." Thesis, Rouen, INSA, 2010. http://www.theses.fr/2010ISAM0010.

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Dans le cadre de systèmes d’aide à la conduite, nous avons contribué aux approches de stéréovision pour l’extraction de contour, la mise en correspondance des images stéréoscopiques et la détection de véhicules. L’extraction de contour réalisée est basée sur le concept declivity line que nous avons proposé. La declivity line est construite en liant des déclivités selon leur position relative et similarité d’intensité. L’extraction de contour est obtenue en filtrant les declivity lines construites basées sur leurs caractéristiques. Les résultats expérimentaux montrent que la declivity lines mét
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Bonakdar, Sakhi Omid. "Segmentation of heterogeneous document images : an approach based on machine learning, connected components analysis, and texture analysis." Phd thesis, Université Paris-Est, 2012. http://tel.archives-ouvertes.fr/tel-00912566.

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Document page segmentation is one of the most crucial steps in document image analysis. It ideally aims to explain the full structure of any document page, distinguishing text zones, graphics, photographs, halftones, figures, tables, etc. Although to date, there have been made several attempts of achieving correct page segmentation results, there are still many difficulties. The leader of the project in the framework of which this PhD work has been funded (*) uses a complete processing chain in which page segmentation mistakes are manually corrected by human operators. Aside of the costs it re
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Khairallah, Mahmoud. "Flow-Based Visual-Inertial Odometry for Neuromorphic Vision Sensors." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST117.

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Plutôt que de générer des images de manière constante et synchrone, les capteurs neuromorphiques de vision -également connus sous le nom de caméras événementielles, permettent à chaque pixel de fournir des informations de manière indépendante et asynchrone chaque fois qu'un changement de luminosité est détecté. Par conséquent, les capteurs de vision neuromorphiques n'ont pas les problèmes des caméras conventionnelles telles que les artefacts d'image et le Flou cinétique. De plus, ils peuvent fournir une compression sans perte de donné avec une résolution temporelle et une plage dynamique plus
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Wigington, Curtis Michael. "End-to-End Full-Page Handwriting Recognition." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7099.

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Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a nove
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Torr, Philip Hilaire Sean. "Motion segmentation and outlier detection." Thesis, University of Oxford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308173.

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Deng, Jingjing (Eddy). "Adaptive learning for segmentation and detection." Thesis, Swansea University, 2017. https://cronfa.swan.ac.uk/Record/cronfa36297.

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Segmentation and detection are two fundamental problems in computer vision and medical image analysis, they are intrinsically interlinked by the nature of machine learning based classification, especially supervised learning methods. Many automatic segmentation methods have been proposed which heavily rely on hand-crafted discriminative features for specific geometry and powerful classifier for delinearating the foreground object and background region. The aimof this thesis is to investigate the adaptive schemes that can be used to derive efficient interactive segmentation methods for medical
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Hastings, Joseph R. 1980. "Incremental Bayesian segmentation for intrusion detection." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28399.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.<br>Includes bibliographical references (leaves 131-133).<br>This thesis describes an attempt to monitor patterns of system calls generated by a Unix host in order to detect potential intrusion attacks. Sequences of system calls generated by privileged processes are analyzed using incremental Bayesian segmentation in order to detect anomalous activity. Theoretical analysis of various aspects of the algorithm and empirical analysis of performance on synthetic data sets ar
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Nedilko, Bohdan. "Seismic detection of rockfalls on railway lines." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58097.

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Railway operators mitigate the risk of derailments caused by hazardous rocks falling onto the track by installing slide detector fences (SDF). These consist of electrical sensing wires strung on poles located uphill of the track; falling rocks snap these wires and trigger an alarm. Rocks of non-threatening size and migrating animals frequently break the wires causing prolonged false alarms and delaying rail traffic until the SDF is manually repaired, often in a hazardous environment. This thesis is concerned with the development of a prototype of the autonomous Seismic Rockfall Detection Syste
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Torrent, Palomeras Albert. "Simultaneous detection and segmentation for generic objects." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/117736.

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This thesis deals with the simultaneous detection and segmentation for generic objects in images. The proposed approach is based on building a dictionary of patches, which defines the object and allows the extraction of the detection and segmentation features used to train the classifier. Moreover, we include in the boosting training the ability of crossing information between detection and segmentation with the aim that good detections may help to better segment and vice versa. We adapt also the detection proposal to deal with specific problems of object recognition in medical and astronomica
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HEGSTAM, BJÖRN. "Defect detection and segmentation inmultivariate image streams." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142069.

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OptoNova is a world leading producer of inspection systems for quality control of surfaces and edges at high rates. They develop their own sensor systems and software and have taken an interest in investigating the possibility of using methods from machine learning to make better use of the available sensor data. The purpose of this project was to develop a method for finding surface defects based on multivariate images. A previous Master’s project done at OptoNova had shown promising results when applying machine learning methods to inspect the sides of kitchen cabinet doors. The model develo
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Books on the topic "Lines detection and segmentation"

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Weiss, John. Automatic jet contrail detection and segmentation. National Aeronautics and Space Administration, 1997.

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Weiss, John. Automatic jet contrail detection and segmentation. National Aeronautics and Space Administration, 1997.

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Yang, Yi. Colour edge detection and segmentation using vector analysis. National Library of Canada, 1995.

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Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53047-1.

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Liang, Kung-Hao. From uncertainty to adaptivity: Multiscale edge detection and image segmentation. typescript, 1997.

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Herout, Adam, Markéta Dubská, and Jiří Havel. Real-Time Detection of Lines and Grids. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4414-4.

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Wang, Yaqi, Dahong Qian, Shuai Wang, et al., eds. Supervised and Semi-supervised Multi-structure Segmentation and Landmark Detection in Dental Data. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88977-6.

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K, Kokula Krishna Hari, ed. An Image Segmentation and Classification for Brain Tumor Detection using Pillar K-Means Algorithm. Association of Scientists, Developers and Faculties, 2016.

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Peterson, Jeffrey Shawn. Detection of downed trolley lines using arc signature analysis. U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Pittsburgh Research Center, 1997.

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Don, Russell B., and IEEE Power Engineering Society. Power Engineering Education Committee., eds. Detection of downed conductors on utility distribution systems. Available from Publication Sales Dept., IEEE Service Center, 1989.

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Book chapters on the topic "Lines detection and segmentation"

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Abdelfattah, Rabab, Xiaofeng Wang, and Song Wang. "TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines." In Computer Vision – ACCV 2020. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69544-6_36.

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Méot, François. "Beam Lines." In Particle Acceleration and Detection. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59979-8_12.

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AbstractThis chapter introduces beam transport and manipulations in beam lines. It provides the theoretical material resorted to in the simulation exercises, leaning on charged particle optics and beam manipulation concepts introduced in earlier Chapters. The simulation of beam lines and specific functionalities they ensure require new optical elements, such as WIENFILTER, EBMULT, high order multipoles, etc. Particle monitoring resorts to keywords introduced in the previous Chapters, including FAISCEAU, FAISTORE, possibly PICKUPS, and some others. Spin motion computation and monitoring resort
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Russ, John C. "Segmentation of Edges and Lines." In Computer-Assisted Microscopy. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0563-7_4.

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Martino, J. C., and Salvatore Tabbone. "Detection of Lofar lines." In Image Analysis and Processing. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60298-4_336.

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Lu, Tong, Shivakumara Palaiahnakote, Chew Lim Tan, and Wenyin Liu. "Character Segmentation and Recognition." In Video Text Detection. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6515-6_6.

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Hogan, Ciarán, and Ganesh Sistu. "Automatic Vehicle Ego Body Extraction for Reducing False Detections in Automated Driving Applications." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_21.

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AbstractFisheye cameras are extensively employed in autonomous vehicles due to their wider field of view, which produces a complete 360-degree image of the vehicle with a minimum number of sensors. The drawback of having a broader field of view is that it may include undesirable portions of the vehicle’s ego body in its perspective. Due to objects’ reflections on the car body, this may produce false positives in perception systems. Processing ego vehicle pixels also uses up unnecessary computing power. Unexpectedly, there is no literature on this relevant practical problem. To our knowledge, t
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Gauch, John M. "Segmentation and edge detection." In The Colour Image Processing Handbook. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5779-1_9.

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Hariharan, Bharath, Pablo Arbeláez, Ross Girshick, and Jitendra Malik. "Simultaneous Detection and Segmentation." In Computer Vision – ECCV 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10584-0_20.

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Morel, Jean Michel, and Sergio Solimini. "Edge Detection and Segmentation." In Variational Methods in Image Segmentation. Birkhäuser Boston, 1995. http://dx.doi.org/10.1007/978-1-4684-0567-5_1.

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Rajalingam, Mallikka. "Character Segmentation." In Text Segmentation and Recognition for Enhanced Image Spam Detection. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53047-1_4.

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Conference papers on the topic "Lines detection and segmentation"

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Wei, Kun, Yuan Guo, Jianhui Zhang, Yaojun Chu, Wenqing Wei, and Haiyun Gan. "Lane Line Detection Algorithm Based on Image Binary Semantic Segmentation." In 2024 5th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). IEEE, 2024. https://doi.org/10.1109/isceic63613.2024.10810155.

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Parulan, Daniel Louise M., Jon Neil P. Borcelis, and Noel B. Linsangan. "Palm Lines Recognition Using Dynamic Image Segmentation." In 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS). IEEE, 2024. http://dx.doi.org/10.1109/i2cacis61270.2024.10649843.

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zhang, xun, xuejie guan, jing luo, and yan zheng. "Algorithm for detecting sea sky-line based on watershed segmentation." In International Conference on Optics, Electronics, and Communication Engineering, edited by Yang Yue. SPIE, 2024. http://dx.doi.org/10.1117/12.3049147.

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Kanwal, Shazia, and Somchat Jiriwibhakorn. "Detection in Transmission Lines Using CNN." In 2024 16th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2024. https://doi.org/10.1109/icitee62483.2024.10808700.

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Zhu, Donglin, Lei Li, Rui Guo, and Shifan Zhan. "Fault Detection by Using Instance Segmentation." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21249-ms.

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Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of f
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Xue, Chuhui, Shijian Lu, and Wei Zhang. "MSR: Multi-Scale Shape Regression for Scene Text Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/139.

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State-of-the-art scene text detection techniques predict quadrilateral boxes that are prone to localization errors while dealing with straight or curved text lines of different orientations and lengths in scenes. This paper presents a novel multi-scale shape regression network (MSR) that is capable of locating text lines of different lengths, shapes and curvatures in scenes. The proposed MSR detects scene texts by predicting dense text boundary points that inherently capture the location and shape of text lines accurately and are also more tolerant to the variation of text line length as compa
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Kumar, Rajiv, and Amardeep Singh. "Detection and segmentation of lines and words in Gurmukhi handwritten text." In 2010 IEEE 2nd International Advance Computing Conference (IACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/iadcc.2010.5422927.

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Yang, Kun, and Jian Wang. "Semantic Segmentation of Lane Lines for Pix2Pix Network Introducing Ghost Module." In 2024 International Conference on Smart Transportation Interdisciplinary Studies. SAE International, 2025. https://doi.org/10.4271/2025-01-7208.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Technology for lane line semantic segmentation is crucial for ensuring the safe operation of intelligent cars. Intelligent cars can now comprehend the distribution and meaning of scenes in an image more precisely thanks to semantic segmentation, which calls for a certain degree of accuracy and real-time network performance. A lightweight module is selected, and two previous models are improved and fused to create the lane line detection model. Finally, experiments are conducted to confirm the model's efficacy. This paper
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Kavallieratou, Ergina. "Text line detection and segmentation." In the 2010 ACM Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1774088.1774102.

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Jahan, Kanwal, Jeethesh Pai Umesh, and Michael Roth. "Anomaly Detection on the Rail Lines Using Semantic Segmentation and Self-supervised Learning." In 2021 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2021. http://dx.doi.org/10.1109/ssci50451.2021.9659920.

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Reports on the topic "Lines detection and segmentation"

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Hazi, A. Radiation Detection Center on the Front Lines. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/885122.

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Bajcsy, Ruzena, Sang W. Lee, and Ales Leonardis. Image Segmentation with Detection of Highlights and Inter-Reflections Using Color. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada218710.

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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Alkhalefah, Suhaylah, Isra AlTuraiki, and Najwa Altwaijry. Advancing Diabetic Foot Ulcer Care: AI and Generative AI Approaches for Classification, Prediction, Segmentation and Detection. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.2.0066.

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Klobucar, Blaz. Urban Tree Detection in Historical Aerial Imagery of Sweden : a test in automated detection with open source Deep Learning models. Faculty of Landscape Architecture, Horticulture and Crop Production Science, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.7kn4q7vikr.

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Urban trees are a key component of the urban environment. In Sweden, ambitious goals have been expressed by authorities regarding the retention and increase of urban tree cover, aiming to mitigate climate change and provide a healthy, livable urban environment in a highly contested space. Tracking urban tree cover through remote sensing serves as an indicator of how past urban planning has succeeded in retaining trees as part of the urban fabric, and historical imagery spanning back decades for such analysis is widely available. This short study examines the viability of automated detection us
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Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. Systematic Review and Meta-Analysis of Aortic Dissection Diagnosis via CT: Evaluating Deep Learning for Detection Against Expert Analysis and Its Application in Detection and Segmentation. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0125.

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Wang, Ting-Wei, Yun-Hsuan Tzeng, Jia-Sheng Hong, et al. The Role of Deep Learning in Aortic Aneurysm Segmentation and Detection from CT Scans: A Systematic Review and Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. http://dx.doi.org/10.37766/inplasy2024.3.0126.

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Motorny, Sergey, S. Glandon, and Jing-Ru Cheng. The design of multimedia object detection pipelines within the HPC environment. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49599.

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Computer vision multimedia pipelines have become both more sophisticated and robust over the years. The pipelines can accept multiple inputs, perform frame analysis, and produce outputs on a variety of platforms with near-real-time performance. Vendors such as Nvidia have significantly grown their framework and library offerings while providing tutorials and documentation via online training and tutorials. Despite the prolific growth, many of the libraries, frameworks, and tutorials come with noticeable limitations. The limitations are especially apparent within the high-performance computing
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Panta, Manisha, Md Tamjidul Hoque, Kendall Niles, Joe Tom, Mahdi Abdelguerfi, and Maik Flanagin. Deep learning approach for accurate segmentation of sand boils in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49460.

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Sand boils can contribute to the liquefaction of a portion of the levee, leading to levee failure. Accurately detecting and segmenting sand boils is crucial for effectively monitoring and maintaining levee systems. This paper presents SandBoilNet, a fully convolutional neural network with skip connections designed for accurate pixel-level classification or semantic segmentation of sand boils from images in levee systems. In this study, we explore the use of transfer learning for fast training and detecting sand boils through semantic segmentation. By utilizing a pretrained CNN model with ResNe
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Alhasson, Haifa F., and Shuaa S. Alharbi. New Trends in image-based Diabetic Foot Ucler Diagnosis Using Machine Learning Approaches: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0128.

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Review question / Objective: A significant amount of research has been conducted to detect and recognize diabetic foot ulcers (DFUs) using computer vision methods, but there are still a number of challenges. DFUs detection frameworks based on machine learning/deep learning lack systematic reviews. With Machine Learning (ML) and Deep learning (DL), you can improve care for individuals at risk for DFUs, identify and synthesize evidence about its use in interventional care and management of DFUs, and suggest future research directions. Information sources: A thorough search of electronic database
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