Gotowe bibliografie tematyczne / You only look at coefficients

Spis treści

  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „You only look at coefficients”

Autor: Grafiati
Data publikacji: 2 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "You only look at coefficients"

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Hesham, Mai, Ghada Kareem, and Marwa Hadhoud. "Enhanced real-time glaucoma diagnosis: dual deep learning approach." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 1846–57. https://doi.org/10.11591/eei.v14i3.8495.

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Effective management of glaucoma is essential for preventing irreversible vision loss. This study introduces a novel deep learning-based network designed to enhance performance while minimizing computational complexity. The system comprises two models: the first is a hybrid model combining a customized U-Net architecture integrated with you only look at coefficients (YOLACT) is utilized to achieve accurate segmentation of the optic disc (OD) and optic cup (OC), providing detailed diagnostic insights for ophthalmologists. The second model employs you only look once version 5 (YOLOv5) for real-t
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Deng, Lu, An Zhang, Jingjing Guo, and Yingkai Liu. "An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds." Remote Sensing 15, no. 6 (2023): 1530. http://dx.doi.org/10.3390/rs15061530.

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In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset
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Chen, Wei-Yu, Yu-Reng Tsao, Jin-Yi Lai, Ching-Jung Hung, Yu-Cheng Liu, and Cheng-Yang Liu. "Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach." Measurement Science Review 22, no. 3 (2022): 107–11. http://dx.doi.org/10.2478/msr-2022-0014.

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Abstract In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The p
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Lin, Guo-Shiang, Kuan-Ting Lai, Jian-Ming Syu, Jen-Yung Lin, and Sin-Kuo Chai. "Instance Segmentation Based on Deep Convolutional Neural Networks and Transfer Learning for Unconstrained Psoriasis Skin Images." Applied Sciences 11, no. 7 (2021): 3155. http://dx.doi.org/10.3390/app11073155.

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In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The predic
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Panero Martinez, Ruben, Ionut Schiopu, Bruno Cornelis, and Adrian Munteanu. "Real-Time Instance Segmentation of Traffic Videos for Embedded Devices." Sensors 21, no. 1 (2021): 275. http://dx.doi.org/10.3390/s21010275.

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The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade
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He, Lang, Shiyun Li, Junting Qiu, and Chenhaomin Zhang. "DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow." Sensors 24, no. 18 (2024): 5929. http://dx.doi.org/10.3390/s24185929.

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Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. To address this challenge, this paper introduces DIO-SLAM: Dynamic Instance Optical Flow SLAM, a VSLAM system specifically designed for dynamic environments. Initially, the detection thread employs YOLACT (You Only Look At CoefficienTs) to distinguish between rigid and non-rigid objects within the scene. Subsequently, the optic
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Wang, Kun, Zheng Zhang, Xiuzhi Yang, Di Wang, Liyi Zhu, and Shuai Yuan. "Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNs." Remote Sensing 16, no. 17 (2024): 3264. http://dx.doi.org/10.3390/rs16173264.

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Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate tha
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Chen, Yanmin, Xiu Li, Mei Jia, Jiuliang Li, Tianyang Hu, and Jun Luo. "Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning." Applied Sciences 13, no. 11 (2023): 6751. http://dx.doi.org/10.3390/app13116751.

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In order to achieve accurate segmentation of each grape image per berry, we construct a dataset composed of red globe grape samples and use a two-stage “localization–segmentation” framework-based mask region convolutional neural network (Mask R-CNN) and one-stage “pixel classification without localization” framework-based You Only Look At CoefficienTs (YOLACT) and segmenting objects by locations (SOLO) models in the grape segmentation experiments. The optimal performance of the model Mask R-CNN was applied for further study. To address the problem of overlapping and occlusion causing inaccurat
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Shi Heng Siow, Abu Ubaidah Shamsudin, Zubair Adil Soomro, et al. "Instance Segmentation Evaluation For Traffic Signs." Journal of Advanced Research in Applied Sciences and Engineering Technology 34, no. 2 (2023): 327–41. http://dx.doi.org/10.37934/araset.34.2.327341.

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This research paper focuses on developing a traffic sign recognition system based on the You Only Look At Coefficients (YOLACT) model, a one-stage instance segmentation model that offers high performance in terms of accuracy and reliability. However, the performance of YOLACT is influenced by various conditions such as day/night and different angles of objects. Therefore, this study aims to evaluate the impact of different angles and environments on the performance of the system. The paper discusses the framework, backbone structure, prototype generation branch, mask coefficient, and mask asse
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Osorio, Kavir, Andrés Puerto, Cesar Pedraza, David Jamaica, and Leonardo Rodríguez. "A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images." AgriEngineering 2, no. 3 (2020): 471–88. http://dx.doi.org/10.3390/agriengineering2030032.

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Weed management is one of the most important aspects of crop productivity; knowing the amount and the locations of weeds has been a problem that experts have faced for several decades. This paper presents three methods for weed estimation based on deep learning image processing in lettuce crops, and we compared them to visual estimations by experts. One method is based on support vector machines (SVM) using histograms of oriented gradients (HOG) as feature descriptor. The second method was based in YOLOV3 (you only look once V3), taking advantage of its robust architecture for object detection
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Rozprawy doktorskie na temat "You only look at coefficients"

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Borngrund, Carl. "Machine vision for automation of earth-moving machines : Transfer learning experiments with YOLOv3." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75169.

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This master thesis investigates the possibility to create a machine vision solution for the automation of earth-moving machines. This research was done as without some type of vision system it will not be possible to create a fully autonomous earth moving machine that can safely be used around humans or other machines. Cameras were used as the primary sensors as they are cheap, provide high resolution and is the type of sensor that most closely mimic the human vision system. The purpose of this master thesis was to use existing real time object detectors together with transfer learning and exa
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Lamberti, Lorenzo. "A deep learning solution for industrial OCR applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19777/.

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This thesis describes a project developed throughout a six months internship in the Machine Vision Laboratory of Datalogic based in Pasadena, California. The project aims to develop a deep learning system as a possible solution for industrial optical character recognition applications. In particular, the focus falls on a specific algorithm called You Only Look Once (YOLO), which is a general-purpose object detector based on convolutional neural networks that currently offers state-of-the-art performances in terms of trade-off between speed and accuracy. This algorithm is indeed well known fo
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Hamren, Rasmus. "APPLYING UAVS TO SUPPORT THE SAFETY IN AUTONOMOUS OPERATED OPEN SURFACE MINES." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53376.

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Unmanned aerial vehicle (UAV) is an expanding interest in numerous industries for various applications. Increasing development of UAVs is happening worldwide, where various sensor attachments and functions are being added. The multi-function UAV can be used within areas where they have not been managed before. Because of their accessibility, cheap purchase, and easy-to-use, they replace expensive systems such as helicopters- and airplane-surveillance. UAV are also being applied into surveillance, combing object detection to video-surveillance and mobility to finding an object from the air with
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Ali, Hani, and Pontus Sunnergren. "Scenanalys - Övervakning och modellering." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45036.

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Självkörande fordon kan minska trafikstockningar och minska antalet trafikrelaterade olyckor. Då det i framtiden kommer att finnas miljontals autonoma fordon krävs en bättre förståelse av omgivningen. Syftet med detta projekt är att skapa ett externt automatiskt trafikledningssystem som kan upptäcka och spåra 3D-objekt i en komplex trafiksituation för att senare skicka beteendet från dessa objekt till ett större projekt som hanterar med att 3D-modellera trafiksituationen. Projektet använder sig av Tensorflow ramverket och YOLOv3 algoritmen. Projektet använder sig även av en kamera för att spel
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Grahn, Fredrik, and Kristian Nilsson. "Object Detection in Domain Specific Stereo-Analysed Satellite Images." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159917.

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Given satellite images with accompanying pixel classifications and elevation data, we propose different solutions to object detection. The first method uses hierarchical clustering for segmentation and then employs different methods of classification. One of these classification methods used domain knowledge to classify objects while the other used Support Vector Machines. Additionally, a combination of three Support Vector Machines were used in a hierarchical structure which out-performed the regular Support Vector Machine method in most of the evaluation metrics. The second approach is more
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Książki na temat "You only look at coefficients"

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Moeller, Carl W. If only you could look inside. Rutledge Books, 1996.

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Mattelaer, Johan. For this Relief, Much Thanks ... Translated by Ian Connerty. Amsterdam University Press, 2018. http://dx.doi.org/10.5117/9789462987326.

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Even though peeing is something we all do several times a day, it is still a taboo subject. From an early age, we are taught to master our urinary urges and to use decent words for this most necessary physiological activity. This paradox has not gone unnoticed by artists through the ages. For this Relief, Much Thanks! Peeing in Art is a journey through time and space, stopping along the way to look at many different art forms. The reader-viewer will see how peeing figures - men and women, young and old, human and angelic - have been depicted over the centuries. You will be amazed to discover h
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Hy, Benjamin. Only Look about You. Independently Published, 2018.

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Moeller, Carl W. If Only You Could Look Inside. Routledge, 1990.

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Only Time You Should Ever Look Back Is to See How Far You've Come. Independently Published, 2021.

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Cameron, L. B. Legend of Shadow and Romance: To Find the Light Within the Dark, You Need Only Look. Independently Published, 2020.

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Prints, Karen. Don't Flatter Yourself I Only Look up to You Because I'm Short: Funny Gymnast Blank Lined Note Book. Independently Published, 2019.

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Aly, Cheema. Day Sanity Was Lost: Everything in Life Has Its Reason, but It Is Only Known When You Look Back. Independently Published, 2022.

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Moran, Bradley. You Know What? I Have Absolutely No Idea What I'm Doing!: I'm Only Writing in Here to Look Busy... Independently Published, 2021.

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Maurice Baring - Overlooked: 'If You Would Know What the Lord God Thinks of Money, You Have Only to Look at Those to Whom He Gives It''. Copyright Group Limited, 2019.

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Części książek na temat "You only look at coefficients"

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Van de Maele, Toon, Tim Verbelen, Ozan Çatal, Cedric De Boom, and Bart Dhoedt. "You Only Look as Much as You Have To." In Active Inference. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64919-7_11.

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Wei, Jun, Yiwen Hu, Shuguang Cui, S. Kevin Zhou, and Zhen Li. "WeakPolyp: You only Look Bounding Box for Polyp Segmentation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43898-1_72.

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Prakash, Immidisetty V., and M. Palanivelan. "A Study of YOLO (You Only Look Once) to YOLOv8." In Algorithms in Advanced Artificial Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003529231-40.

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Campos, Filipe, Francisco Gonçalves Cerqueira, Ricardo P. M. Cruz, and Jaime S. Cardoso. "YOLOMM – You Only Look Once for Multi-modal Multi-tasking." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49018-7_40.

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Seethalakshmi, Palaparthi, Rao CH Dhawaleswara, Pavuluri Sreekanth Reddy, Vidhyanadh Babu, and S. Nourin. "Detection of lung nodules-using yolo v7: (You Only Look Once)." In Hybrid and Advanced Technologies. CRC Press, 2025. https://doi.org/10.1201/9781003559139-82.

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Qiu, Jingting, Feifan Yu, Fengrui Xu, Jiqiang Wang, and Xinmin Chen. "Improved You Only Look Once Model for UAVs/Ships Relative Attitude Detection." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8658-9_21.

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Dos Reis Pacheco, Manuel Soares, Hadiyanto Hadiyanto, and Ridwan Sanjaya. "Quality Detection Model of Nutmeg (Myristica Fragrans Houtt) Using You Only Look Once (YOLO)." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3250-3_54.

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Swetha, O., and C. Ramachandran. "Counting and Tracking of Vehicles and Pedestrians in Real Time Using You Only Look Once V3." In Data Intelligence and Cognitive Informatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8530-2_69.

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Hubert, G., and S. Silvia Priscila. "You Only Look Once (YOLO) with Convolution Neural Network (CNN) Classification for Preterm Baby’s Retinopathy Images." In Advancements in Smart Computing and Information Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59097-9_27.

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Susmitha, Allumallu Veera Venkata. "Smart Recognition System for Business Predictions (You Only Look Once – V3) Unified, Real-Time Object Detection." In Internet of Things for Industry 4.0. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32530-5_9.

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Streszczenia konferencji na temat "You only look at coefficients"

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Zhan, Dakui, and Khamron Sunat. "You Only Look Once with Clustering Segmentation Module." In 2025 5th International Conference on Advances in Electrical, Electronics and Computing Technology (EECT). IEEE, 2025. https://doi.org/10.1109/eect64505.2025.10966947.

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Zhang, Zhuosheng, and Aston Zhang. "You Only Look at Screens: Multimodal Chain-of-Action Agents." In Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.186.

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Aminuddin, Nurhanisah, Nor Azuana Ramli, and Agus Pratondo. "Real-Time Personal Protective Equipment Compliance Detection Using You Only Look Once." In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730560.

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Hamzah, Salehah, Nur Nabilah Abu Mangshor, Muhammad Syahmie Kamarudin, Shafaf Ibrahim, and Ahmad Firdaus Ahmad Fadzil. "Recyclable Waste Detection using You Look Only Once (YOLOv8): A Preliminary Study." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872699.

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Gudala, Leeladhar, Pradyumna Amasebail Kodgi, Ashok Kumar Pamidi Venkata, Zaed Balasm, and K. Ghamya. "Unmanned Aerial Vehicles with You Only Look Once Version5 in Agriculture Monitoring." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070787.

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Nishitha, U., V. Lokesh, Tata Kaushik, and Rimjhim Padam Singh. "Vehicle Detection in Unmanned Aerial Imagery Through Advance You Only Look Once Architectures." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724269.

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Rani, K. Pushpa, Mohammad Arshad, and A. Sangeetha. "Expression of Concern for: Pothole Detection Using YOLO (You Only Look Once) Algorithm." In 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). IEEE, 2022. http://dx.doi.org/10.1109/assic55218.2022.10703639.

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Setiawan, Yosua, Muhammad Nurul Puji, and Winda Astuti. "Traffic Signs Detection System Using YOLO (You Only Look Once) That Provides Notification." In 2024 IEEE International Conference on Smart Mechatronics (ICSMech). IEEE, 2024. https://doi.org/10.1109/icsmech62936.2024.10812285.

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Devi., T., Hassan Mohamed Ali, Zaid Alsalami, S. Senthil kumar, and K. Sangeetha. "Concrete Structure Defect Detection Using You Only Look Once Version 5 with AlexNet." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10860257.

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Permana, Yudistira Dwi, Widya Mulyaningtyas, Rozikul Wijaya, and Kusrini. "Vehicle Detection Using You Only Look Once V8 Based On Architecture Modification Method." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903962.

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Raporty organizacyjne na temat "You only look at coefficients"

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Cheng, DingXin. Development of the Roadway Pothole Management Program. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2306.

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Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pot
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Forero Fuarez, Luis Carlos. Procesamiento de imágenes. Escuela Tecnológica Instituto Técnico Central - ETITC, 2023. http://dx.doi.org/10.55411/2023.4.

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El semillero tiene como uno de sus objetivos, la enseñanza y la aplicación de técnicas y herramientas de inteligencia artificial en áreas de la ingeniería electromecánica y afines. Para ello se seguirá un proceso que requerirá en sus primeras etapas la recopilación de la información, su limpieza, transformación y análisis, persiguiendo mediante el aprendizaje continuo de los estudiantes y su desarrollo en posteriores etapas, la implementación de modelos y/o arquitecturas que permitan desarrollar un modelo de IA basado en técnicas de visión por computadora y aprendizaje automático para reconoce
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Cvijić, Srdjan, Nikola Dimitrov, Leposava Ognjanoska Stavrovska, and Ivana Ranković. Bilateral Disputes and EU enlargement: A Consensual Divorce. Belgrade Centre for Security Policy, 2024. http://dx.doi.org/10.55042/xubk6023.

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Bilateral disputes between European Union member states and candidate countries are one of the key obstacles to EU enlargement. They have been plaguing the EU accession process ever since the breakup of Yugoslavia and the subsequent border dispute between EU member Slovenia and candidate country Croatia which then ensued. More recently we have the case of North Macedonia. It became a candidate country in 2005 but ever since, its accession negotiations have been bogged down by endless bilateral disputes. While the case of North Macedonia and its decades long conflicts with Greece and Bulgaria a
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Re-CHARGE - Voices about living with CHARGE syndrome. Nordic Welfare Centre, 2024. http://dx.doi.org/10.52746/dvez7239.

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Given the right support, persons with CHARGE syndrome can overcome not only medical challenges but also various other obstacles, and lead fulfilling lives. This publication shows, through case studies and interviews, some daily lives, challenges, and achievements of persons living with CHARGE syndrome, emphasizing the importance of understanding and support from their community. Living with CHARGE syndrome can entail different aspects and challenges. Often, the focus revolves around the medical aspects, and less on how individuals can function in their day-to-day lives. There is a lack of case
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