Relevant bibliographies by topics / You only look once version 5

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Academic literature on the topic 'You only look once version 5'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "You only look once version 5"

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Lee, Tae-Young, Seung Bae Jeon, and Myeong-Hun Jeong. "Marine Debris Detection Using Optimized You Only Look Once Version 5." Sensors and Materials 35, no. 9 (2023): 3441. http://dx.doi.org/10.18494/sam4477.

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Meng, Mingzhu, Ming Zhang, Dong Shen, Guangyuan He, and Yi Guo. "Detection and Classification of Breast Lesions with You Only Look Once Version 5." Future Oncology 18, no. 39 (2022): 4361–70. http://dx.doi.org/10.2217/fon-2022-0593.

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Al-Haimi, Hamzah Abdulmalek, Zamani Md Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, Azizul Azizan, and Karim Samsul Ariffin Abdul. "Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1585–92. https://doi.org/10.11591/ijai.v12.i4.pp1585-1592.

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This project aims to develop a vision system that can detect traffic light counter and to recognise the numbers shown on it. The system used you only look once version 3 (YOLOv3) algorithm because of its robust performance and reliability and able to be implemented in Nvidia Jetson nano kit. A total of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another 80% (1764) from the images are used for training and 20% (440) are used for testing. The results obtained from the training demonstrated Total precision=89%, Recall=99.2%, F1 score=70%, intersection over union (IoU)=70.49%, me
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Al-Haimi, Hamzah Abdulmalek, Zamani Md Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, Azizul Azizan, and Samsul Ariffin Abdul Karim. "Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1585. http://dx.doi.org/10.11591/ijai.v12.i4.pp1585-1592.

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<p>This project aims to develop a vision system that can detect traffic light<br />counter and to recognise the numbers shown on it. The system used you only<br />look once version 3 (YOLOv3) algorithm because of its robust performance<br />and reliability and able to be implemented in Nvidia Jetson nano kit. A total<br />of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another<br />80% (1764) from the images are used for training and 20% (440) are used for<br />testing. The results obtained from the training demonstrated Total<br /&
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Aicha, Khalfaoui, Badri Abdelmajid, and El Mourabit Ilham. "A lightweight you only look once for real-time dangerous weapons detection." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1838–44. https://doi.org/10.11591/ijai.v13.i2.pp1838-1844.

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Deep neural networks are currently employed to detect weapons, and although these techniques provide a high level of accuracy, it still suffers from large weight parameters and a slow inference speed. When considering real-world applications like weapon detection, these methods are frequently unsuitable for deployment on embedded devices due to their large number of parameters and poor efficiency. The most recent object detection technique, which falls under the YOLOv5 (You Only Look Once version 5) family, is commonly used for detecting weapons. However, it faces some difficulties such as hig
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Liang, Yu, Sai Li, Guanting Ye, Qing Jiang, Qiang Jin, and Yifei Mao. "Autonomous surface crack identification for concrete structures based on the you only look once version 5 algorithm." Engineering Applications of Artificial Intelligence 133 (July 2024): 108479. http://dx.doi.org/10.1016/j.engappai.2024.108479.

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Li, Yan. "Virtual sports interactive system design integrating ghost net network and improved YOLOv5 algorithm." International Journal for Simulation and Multidisciplinary Design Optimization 15 (2024): 19. http://dx.doi.org/10.1051/smdo/2024016.

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With the development of virtual reality, the human–computer interaction through virtual sports is gradually maturing, and users are gradually looking to interact with the two-dimensional world. The research on this type of algorithm has gained attention. However, due to the delay of the old transmission technology in the transmission of pictures, which is higher than the reaction time of human brain, the pictures are inconsistent and illogical, and the user interaction experience is poor. To solve it, this research realizes the fusion of ghost network and You Only Look Once version 5, and the
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Wan, Xueqiang, Jiong Yu, Haotian Tan, and Junjie Wang. "LAG: Layered Objects to Generate Better Anchors for Object Detection in Aerial Images." Sensors 22, no. 10 (2022): 3891. http://dx.doi.org/10.3390/s22103891.

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You Only Look Once (YOLO) series detectors are suitable for aerial image object detection because of their excellent real-time ability and performance. Their high performance depends heavily on the anchor generated by clustering the training set. However, the effectiveness of the general Anchor Generation algorithm is limited by the unique data distribution of the aerial image dataset. The divergence in the distribution of the number of objects with different sizes can cause the anchors to overfit some objects or be assigned to suboptimal layers because anchors of each layer are generated unif
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Wang, Haiyan, Zhan Shi, Guiyuan Gao, Chuang Li, Jian Zhao, and Zhiwei Xu. "Robot Operating Systems–You Only Look Once Version 5–Fleet Efficient Multi-Scale Attention: An Improved You Only Look Once Version 5-Lite Object Detection Algorithm Based on Efficient Multi-Scale Attention and Bounding Box Regression Combined with Robot Operating Systems." Applied Sciences 14, no. 17 (2024): 7591. http://dx.doi.org/10.3390/app14177591.

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This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models’ ability to generalize. However, the model’s performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incor
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Chen, Yen-Chiu, Kun-Ming Yu, Tzu-Hsiang Kao, and Hao-Lun Hsieh. "Deep learning based real-time tourist spots detection and recognition mechanism." Science Progress 104, no. 3_suppl (2021): 003685042110442. http://dx.doi.org/10.1177/00368504211044228.

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More and more information on tourist spots is being represented as pictures rather than text. Consequently, tourists who are interested in a specific attraction shown in pictures may have no idea how to perform a text search to get more information about the interesting tourist spots. In the view of this problem and to enhance the competitiveness of the tourism market, this research proposes an innovative tourist spot identification mechanism, which is based on deep learning-based object detection technology, for real-time detection and identification of tourist spots by taking pictures on loc
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Books on the topic "You only look once version 5"

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Johansen, Bruce, and Adebowale Akande, eds. Nationalism: Past as Prologue. Nova Science Publishers, Inc., 2021. http://dx.doi.org/10.52305/aief3847.

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Nationalism: Past as Prologue began as a single volume being compiled by Ad Akande, a scholar from South Africa, who proposed it to me as co-author about two years ago. The original idea was to examine how the damaging roots of nationalism have been corroding political systems around the world, and creating dangerous obstacles for necessary international cooperation. Since I (Bruce E. Johansen) has written profusely about climate change (global warming, a.k.a. infrared forcing), I suggested a concerted effort in that direction. This is a worldwide existential threat that affects every living t
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Book chapters on the topic "You only look once version 5"

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Asaju, Christine Bukola, Pius Adewale Owolawi, Chunling Du, and Etienne Van Wyk. "Enhancing Security with Automated Boom Gate Access Through License Plate Recognition Utilising YOLOv8 Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85856-7_15.

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Abstract The combination of computer vision and deep learning approaches has changed automated systems across numerous domains. Such a domain is object detection. This study presents an automatic boom gate access method based on the YOLOv8 (You Only Look Once version 8) object detection model and license plate recognition (LPR) technology. It tries to resolve the issue of secure and efficient boom gate entry in restricted regions. The approach takes advantage of YOLOv8’s capacity to reliably detect and recognize license plates in real-time, allowing for automated gate operation. The experiment
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Trapp, M. B. "The Philosopher and his Teaching." In Maximus of Tyre. Oxford University PressOxford, 1997. http://dx.doi.org/10.1093/oso/9780198149897.003.0001.

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Abstract Introduction An introductory address, which once headed the written version of the Orations (see Introduction, p. lix), and could have been delivered viva voce on any number of separate occasions. In it, Maximus seeks to persuade his audience both that they need philosophical instruction, and that he is the man to provide it. §§ r- 5 develop the first point: philosophical teaching is essential to bring order and stability into the confusion of human life, by directing humanity to the only truly satisfying and worthwhile goal (Virtue); this goal is both practically attainable, and endo
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Chen, Xinyuan. "Object Detection Study for Retail Products." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240120.

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To help the management of stores, especially the stores that provide self-check digital services, and to better understand and study the object-detection algorithm based on You Only Look Once, used many ways to train and get a YOLO model that satisfy the need of detecting multiple products in images. The RPC dataset is used to train the model, part of which is used in the training process to generate a final version, the result performance of the model generally shows the YOLO algorithm has its strengths when faced with images of retail goods.
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Bhavadharshini M, Josephine Racheal J, Kamali M, Sankar S, and Bhavadharshini M. "Sign Language Translator Using YOLO Algorithm." In Advances in Parallel Computing Technologies and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/apc210136.

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Sign language is a terminology that encloses a motion of hand gestures which is an environment for the auditory impairment, individual (deaf or dumb) to deal with others. Nevertheless, so as to impart with the hearing impaired individual, the communicator obtains to acquire acquaintance in sign language. As follows is frequent to make undoubted that the message provided by the hearing impaired person acknowledged. This implemented system propounds an implementation of real time American Sign Language perception in Convolutional Neural Network (CNN) with the support of You Only Look Once versio
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Singh, Richa, Veena Parihar, Nidhi Srivastava, and Rekha Kashyap. "Analyze and Optimize the Performance of MRI Images for Tumor Detection Using Image Segmentation With YOLOV7 Algorithm." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8272-1.ch004.

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Brain-tumor detection is a very complicated task in the medical domain. The early detection of tumors in patients' lives can provide improved outcomes and a balanced life. Magnetic Resonance Imaging (MRI) is an essential tool in the domain of medical diagnostics, particularly for tumor detection. The application of advanced image segmentation algorithms, such as the YOLOv7 (You Only Look Once version 7), has shown promising outcomes in enhancing the accuracy as well as efficiency of tumor detection. This chapter explores the performance optimization of MRI images for tumor detection using YOLO
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Gandrapu Satya Sai Surya Subrahmanya Venkata Krishna Mohan, Mahammad Firose Shaik, G. Usandra Babu, Manikandan Hariharan, and Kiran Kumar Patro. "Deep Learning-Powered Visual Augmentation for the Visually Impaired." In Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815305210125010013.

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The interdisciplinary convergence of computer vision and object detection is pivotal for advancing intelligent image analysis. This research surpasses conventional object recognition methodologies by delving into a more nuanced understanding of images, akin to human visual comprehension. It explores deep learning and established object detection systems such as convolutional neural networks (CNN), Region-based CNN (R-CNN), and you only look once (YOLO). The proposed model excels in realtime object recognition, outperforming its predecessors, as previous systems typically detect only a limited
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Manzo, V. J. "Generating Music." In Max/MSP/Jitter for Music. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780199777679.003.0007.

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In this chapter, we will create a program that randomly generates pitches at a specified tempo. The program will have the ability to change a number of musical variables including timbre, velocity, and tempo. We will also write a program that allows your MIDI keyboard to function as a synthesizer. These two programs will be the basis of future projects related to composition and performance. Since you’ve already learned a number of objects in the previous chapter, let’s agree that when you’re asked to create an object that you already know, like button, for example, it will be sufficient for m
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Allchin, Douglas. "Male, Female, and/ or — ?" In Sacred Bovines. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190490362.003.0024.

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Intersex individuals are coming out of the closet. Witness, for example, the 2003 Pulitzer Prize in Fiction for Jeffrey Eugenides’s Middlesex. The story follows someone with 5-alpha-reductase deficiency, or late-onset virilization. Imagine yourself raised as a girl, discovering at puberty (through cryptic, piecemeal clues) that you are male instead. Or male also? Or male only now? Or “just” newly virile? The condition confounds the conventionally strict dichotomy between male and female, masculine and feminine. It teases a culture preoccupied with gender. What are male and female, biologically
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"the claim that if anything of the sort had occurred I would have brought a plea in bar of action against him, but that I should come to court with this plea and demonstrate to you both that I have done this man no wrong and that his prosecution of me is illegal. [2] If Pantainetos had suffered any of the wrongs of which he is now complaining, he would clearly have brought a suit at once during the period when our business dealings took place, since these suits are monthly and we were both in town, and when all mankind are in the habit of showing their indignation right at the moment of their wrongs rather than after a delay. Since he has suffered no wrong – as you too will (I’m sure) affirm when you hear what happened – but is plaguing me from the confidence aroused by his success in the suit against Euergos, the only course left for me is to prove in your court, judges, that I am not in any way guilty and provide witness for my statements in an attempt to save myself. [3] My request to all of you will be modest and fair: to hear me with goodwill on the issue of my barring plea and to pay attention to the whole of my case. For though many suits have taken place in the city, I think it will be found that no-one has brought a suit more shameless or more unscrupulous than the one he has dared to lodge and bring to court. I shall give you as brief an account as I am able of all our dealings from the beginning. [4] Euergos and I loaned one hundred and five mnai to Pantainetos here, judges, on the security of a processing plant among the mine workings at Maroneia and thirty slaves. Forty-five mnai of the loan were mine, while one talent belonged to Euergos. As it happened, Pantainetos owed a talent to Mnesikles of Kollytos and forty-five mnai to Phileas of Eleusis and Pleistor. [5] The individual who sold the processing plant and the slaves to us was Mnesikles (he was the one who had bought the property for Pantainetos from Telemachos, its former owner), and Pantainetos leased it from us for the interest accruing on the money, one hundred and five drachmas per month. We made a contract in which were written the terms of the lease and a right for Pantainetos to redeem the property from us within a stated time. [6] Once this had been completed in the month of Elaphebolion in the archonship of Theophilos, I sailed off to the Black Sea, while this man and Euergos were here. As to their dealings with each other while I was away, I could not say. For their versions do not agree with each other, nor does Pantainetos’ version always agree with itself. Sometimes he says he was evicted." In Trials from Classical Athens. Routledge, 2002. http://dx.doi.org/10.4324/9780203130476-38.

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"11.5 The analysis Having laid out the chart (and this chart will be left at this point although there is more that can be done), we can see two major problem areas, because the chart is structured to lead to the UP through the PP (that is, the elements of the legal rule concerned). We are able to notice at once where there is strength and where there is not. Looking back at Figure 7.32, it can be seen that there are major queries relating to PP 5. This is the PP concerning intention which in s1(1) is the only element of the mens rea. So, unless more certainty can be achieved in this area there is a problem. In addition, PP 2 has a question mark indicating uncertainty. This is the element of the actus reus requiring the dishonest appropriation but Mary alleges she acted in the certainty that Andrew would have lent her the money: in other words she had his permission. We can see that there are many elements of strength stacking up under PP 2 but a key issue is 14—going into Andrew’s room without permission. So clearly we are interested when we turn to the legal analysis in looking at case law dealing with this issue. Although we have tried to counter the problem with 14 by saying in 18 that Mary was wilful about whether she had permission or not, in the circumstances can we allege this? So we should explore the following matters in the case authorities. (a) Actus reus Re: PP 2 • What is the legal meaning of dishonestly? • Does it include believing that you have permission to take something? • What is the test for a reasonable belief that you have permission? Is it according to what other reasonable people would think (an objective test) or is it according to whatever Mary thought—no matter how unreasonable? (A very subjective test.) • Can we argue she had conditional permission to take £20 for a skirt but she spent the money on something else? Does that matter? If she thought Andrew would give permission for the skirt does it matter that she went to the cinema and got a take away meal instead? (b) Mens rea Re: PP 5 Mary said that she did not intend to permanently deprive Andrew of his money. • However, she said she would pay Andrew back on Monday, yet she clearly would have no money until Thursday. Does this matter? • Does this suggest an intention to permanently deprive? Are there cases covering this? As you can see whilst the chart is excellent at its task (factual analysis) it only highlights the areas for legal analysis. Which is why the charting process leads to legal analysis. This is the moment to look for answers at the level of statutory sources and case law which we will do briefly. We will just make a few explorations to indicate how this matter can be pursued." In Legal Method and Reasoning. Routledge-Cavendish, 2012. http://dx.doi.org/10.4324/9781843145103-201.

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Conference papers on the topic "You only look once version 5"

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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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Patchamatla, Pavan Srikanth SubbaRaju, Raami Riadhusin, N. Subhash Chandra, Pramodhini R, and K. Alagarraja. "Improved Welding Defects Recognition with Transfer Learning Based You Only Look Once Version 5." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10968402.

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Hulliyah, Khodijah, Dito Hafidzulrahman, Nenny Anggraini, Luh Kesuma Wardhani, and Muhamad Fardal Akter Min Gali. "Comparison of You Only Look Once (YOLO) Algorithm Version 5 and Version 8 as Object Detection in Hilal Detection." In 2024 12th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2024. https://doi.org/10.1109/citsm64103.2024.10775779.

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Wang, Rui. "Billiard Ball Assisted Training Method Combining Squeeze and Excitation Attention Mechanism and You Only Look Once Version 5 Algorithm." In 2024 7th International Conference on Education, Network and Information Technology (ICENIT). IEEE, 2024. https://doi.org/10.1109/icenit61951.2024.00037.

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Ihsan, Mohammed, Ramesh Babu N, Navamani C, Narendra Chennupati, and P. Vinayasree. "Traffic Sign Recognition using You Look Only Once Version 8 with Vision Transformer." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11035452.

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Ran, Baoxin, Jianli Bu, and Shaolong Han. "Fault Diagnosis of Tobacco Packaging Machine Detection using You Only Look Once Version 8." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859929.

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Setiawan, Foni Agus, Zakiah Aulia Rohmah, and Gibtha Fitri Laxmi. "Indonesian Sign Language (BISINDO) Alphabet Detection Using the You Only Look Once (YOLO) Algorithm Version 8." In 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2024. http://dx.doi.org/10.1109/ic3ina64086.2024.10732209.

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Gowri, B. Shyamala, Harini Harisitha S, Abishek Kanna M, and Amirthavarshini N S. "You Only Look Once Version 8 (YOLOv8)-Driven Emergency Vehicle Detection and Graph Neural Networks (GNNs) based Traffic Signal Prioritization." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988348.

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Yu, Yongbo. "Single Phase Grounding Fault Line Selection Method based on Improved Multivariate Variational Mode Decomposition with You Only Look Once Version 10." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872190.

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Liao, Shenglang. "Road damage detection algorithm based on optimised You Only Look Once version 8." In 2024 5th International Conference on Computer Engineering and Application (ICCEA). IEEE, 2024. http://dx.doi.org/10.1109/iccea62105.2024.10603714.

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