Bibliografías temáticas / YOLOv8

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Literatura académica sobre el tema "YOLOv8"

Autor: Grafiati

Publicado: 11 de enero de 2025

Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "YOLOv8"

Sharma, Pravek, Dr Rajesh Tyagi, and Dr Priyanka Dubey. "Optimizing Real-Time Object Detection- A Comparison of YOLO Models." International Journal of Innovative Research in Computer Science and Technology 12, no. 3 (2024): 57–74. http://dx.doi.org/10.55524/ijircst.2024.12.3.11.

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Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO varia

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Tahir, Noor Ul Ain, Zhe Long, Zuping Zhang, Muhammad Asim, and Mohammed ELAffendi. "PVswin-YOLOv8s: UAV-Based Pedestrian and Vehicle Detection for Traffic Management in Smart Cities Using Improved YOLOv8." Drones 8, no. 3 (2024): 84. http://dx.doi.org/10.3390/drones8030084.

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In smart cities, effective traffic congestion management hinges on adept pedestrian and vehicle detection. Unmanned Aerial Vehicles (UAVs) offer a solution with mobility, cost-effectiveness, and a wide field of view, and yet, optimizing recognition models is crucial to surmounting challenges posed by small and occluded objects. To address these issues, we utilize the YOLOv8s model and a Swin Transformer block and introduce the PVswin-YOLOv8s model for pedestrian and vehicle detection based on UAVs. Firstly, the backbone network of YOLOv8s incorporates the Swin Transformer model for global feat

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Tresnawati, Dewi, Shopi Nurhidayanti, and Nina Lestari. "A Comparison of YOLOv8 Series Performance in Student Facial Expressions Detection on Online Learning." Jurnal Online Informatika 10, no. 1 (2025): 93–104. https://doi.org/10.15575/join.v10i1.1390.

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Student engagement in online learning is an important factor that can affect learning outcomes. One indicator of engagement is facial expression. However, research on facial expression detection in online learning environments is still limited, especially in the use of the YOLOv8 algorithm. This study aims to compare the performance of several YOLOv8 variants, namely YOLOv8x, YOLOv8m, YOLOv8s, YOLOv8n, and YOLOv8l in recognizing six facial expressions: happy, sad, angry, surprised, afraid, and neutral. Student facial expression data was collected through the Moodle platform every 15 seconds du

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Wulanningrum, Resty, Anik Nur Handayani, and Aji Prasetya Wibawa. "Perbandingan Instance Segmentation Image Pada Yolo8." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 4 (2024): 753–60. http://dx.doi.org/10.25126/jtiik.1148288.

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Seorang pejalan kaki sangat rawan terhadap kecelakaan di jalan. Deteksi pejalan kaki merupakan salah satu cara untuk mengidentifikasi atau megklasifikasikan antara orang, jalan atau yang lainnya. Instance segmentation adalah salah satu proses untuk melakukan segmentasi antara orang dan jalan. Instance segmentation dan penggunaan yolov8 merupakan salah satu implementasi dalam deteksi pejalan kaki. Perbandingan segmentasi pada dataset Penn-Fundan Database menggunakan yolov8 dengan model yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg. Penelitian ini menggunakan dataset publik ped

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Wang, Jiahui, Mengdie Jiang, Tauseef Abbas, Hao Chen, and Yuying Jiang. "YOLOv-MA: A High-Precision Foreign Object Detection Algorithm for Rice." Agriculture 15, no. 13 (2025): 1354. https://doi.org/10.3390/agriculture15131354.

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Rice plays a crucial role in global agricultural production, but various foreign objects often mix in during its processing. To efficiently and accurately detect small foreign objects in the rice processing pipeline, ensuring food quality and consumer safety, this study innovatively proposes a YOLOv-MA-based foreign object detection algorithm for rice, leveraging deep learning techniques. The proposed algorithm adaptively enhances multi-scale feature representation across small, medium, and large object detection layers by incorporating the multi-scale dilated attention (MSDA) mechanism. Addit

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Weng, Wei-Chun, Chien-Wei Huang, Chang-Chao Su, et al. "Optimizing Esophageal Cancer Diagnosis with Computer-Aided Detection by YOLO Models Combined with Hyperspectral Imaging." Diagnostics 15, no. 13 (2025): 1686. https://doi.org/10.3390/diagnostics15131686.

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Objective: Esophageal cancer (EC) is difficult to visually identify, rendering early detection crucial to avert the advancement and decline of the patient’s health. Methodology: This work aimed to acquire spectral information from EC images via Spectrum-Aided Visual Enhancer (SAVE) technology, which improves imaging beyond the limitations of conventional White-Light Imaging (WLI). The hyperspectral data acquired using SAVE were examined utilizing sophisticated deep learning methodologies, incorporating models such as YOLOv8, YOLOv7, YOLOv6, YOLOv5, Scaled YOLOv4, and YOLOv3. The models were as

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Panja, Eben, Hendry Hendry, and Christine Dewi. "YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions." Scientific Journal of Informatics 11, no. 1 (2024): 127–38. http://dx.doi.org/10.15294/sji.v11i1.49038.

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Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation

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Khusainov, R. M. "Selection of a Neural Network Model Based on the Hierarchy Process Analysis Method." Vestnik NSU. Series: Information Technologies 22, no. 4 (2025): 62–70. https://doi.org/10.25205/1818-7900-2024-22-4-62-70.

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This article discusses the selection of the optimal neural network model YOLOv8 (YOLOv8s, YOLOv8l, YOLOv8x, YOLOv8m, YOLOv8n) using the hierarchy process analysis method, which allows structuring and systematizing complex decisions based on multi-criteria assessments. The main focus is on identifying and comparative analysis of the most significant criteria for assessing the effectiveness of neural network models, such as training time, as well as the Precision, Recall and F1-score metrics. These metrics play a key role in computer vision tasks, especially when it comes to object detection. Du

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Podder, Soumyajit, Abhishek Mallick, Sudipta Das, Kartik Sau, and Arijit Roy. "Accurate diagnosis of liver diseases through the application of deep convolutional neural network on biopsy images." AIMS Biophysics 10, no. 4 (2023): 453–81. http://dx.doi.org/10.3934/biophy.2023026.

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<abstract> <p>Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the

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De Dios Garcia, Julio Cesar, Nantai Nava Nolazco, Ernesto Monroy Cruz, et al. "Comparative study of Convolutional Neural Networks performance and efficiency with YOLOv8 models applied for pest detection purposes in bean plants." International Journal of Combinatorial Optimization Problems and Informatics 16, no. 2 (2025): 112–22. https://doi.org/10.61467/2007.1558.2025.v16i2.603.

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Neural Networks have significantly evolved, particularly in their application to computer vision. This paper presents a comprehensive comparison of different versions of YOLOv8, such as YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x for the detection of pests in bean plants, leveraging the capabilities of Convolutional Neural Networks. To train the neural network using different versions of YOLOv8, identical conditions were applied, such as the amount of environment light, the number of labeled images, epochs, and batch size. The results indicate that, as the complexity of the YOLO model incr

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Tesis sobre el tema "YOLOv8"

Yesudasu, Santheep. "Cοntributiοn à la manipulatiοn de cοlis sοus cοntraintes par un tοrse humanοïde : applicatiοn à la dépaléttisatiοn autοnοme dans les entrepôts lοgistiques". Electronic Thesis or Diss., Normandie, 2024. https://theses.hal.science/tel-04874770.

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Cette thèse de doctorat explore le développement et l'implémentation d'URNik-AI, un système de dépalettisation automatisé basé sur l'intelligence artificielle (IA), conçu pour manipuler des boîtes en carton de tailles et de poids variés à l'aide d'un torse humanoïde à double bras. L'objectif principal est d'améliorer l'efficacité, la précision et la fiabilité des tâches de dépalettisation industrielle grâce à l'intégration de la robotique avancée, de la vision par ordinateur et des techniques d'apprentissage profond.Le système URNik-AI est composé de deux bras robotiques UR10 équipés de capteu

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Oškera, Jan. "Detekce dopravních značek a semaforů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432850.

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The thesis focuses on modern methods of traffic sign detection and traffic lights detection directly in traffic and with use of back analysis. The main subject is convolutional neural networks (CNN). The solution is using convolutional neural networks of YOLO type. The main goal of this thesis is to achieve the greatest possible optimization of speed and accuracy of models. Examines suitable datasets. A number of datasets are used for training and testing. These are composed of real and synthetic data sets. For training and testing, the data were preprocessed using the Yolo mark tool. The trai

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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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Melcherson, Tim. "Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

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Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex region and how it is effected by climate change. Here, animals and vegetation are tightly coupled with their environment in a fragile ecosystem, and when the environment undergo rapid changes it risks damaging these ecosystems severely. Understanding what kind of data that has potential to be used in artificial intelligence, can be of importance as many research stations have data archives from decades of work in the Arctic. In this thesis, a YOLOv4 object d

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Núñez-Melgar, Espinoza Erika Pamela, Oré Natali Leonor Reyes, Abad Jorge Raúl Salazar, and Vela Anderson Vásquez. "YOLO." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2018. http://hdl.handle.net/10757/625370.

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El siguiente trabajo de investigación pretende comprobar la viabilidad del proyecto denominado YOLO. Este proyecto propone crear un medio virtual para interrelacionar dos segmentos de intereses o necesidades diferentes: un segmento que desea vender productos y otro segmento que desea obtenerlos participando en un proceso de rifa virtual a precios accesibles. En la encuesta virtual realizada para conocer el interés del servicio en el mercado, los resultados que se obtuvieron fueron que, un 72% estaría dispuesto a participar en juegos de azar virtuales y que un 52% ha vendido algún producto nu

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Norling, Samuel. "Tree species classification with YOLOv3 : Classification of Silver Birch (Betula pendula) and Scots Pine (Pinus sylvestris)." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260244.

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Automation of tree species classification during a forest inventory could potentially provide more efficiency and better results for forest companies and stakeholding agencies. This thesis investigates how well a state of the art object detection system, YOLOv3, performs this classification task. A new image dataset with pictures of Silver Birches and Scots Pines, called LilljanNet, was created to train YOLOv3. After training YOLOv3 on half the dataset we performed validation by testing it against the other half. The trained model scored a mean average precision above 0.99. Training was also d

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Ståhl, Sebastian. "A tracking framework for a dynamic non- stationary environment." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288955.

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As the use of unmanned aerial vehicles (UAVs) increases in popularity across the globe, their fields of application are constantly growing. This thesis researches the possibility of using a UAV to detect, track, and geolocate a target in a dynamic nonstationary environment as the seas. In this case, different projection and apparent size of the target in the captured images can lead to ambiguous assignments of coordinated. In this thesis, a framework based on a UAV, a monocular camera, a GPS receiver, and the UAV’s inertial measurement unit (IMU) is developed to perform the task of detecting,

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Ye, Fanjie. "A Method of Combining GANs to Improve the Accuracy of Object Detection on Autonomous Vehicles." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1752364/.

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As the technology in the field of computer vision becomes more and more mature, the autonomous vehicles have achieved rapid developments in recent years. However, the object detection and classification tasks of autonomous vehicles which are based on cameras may face problems when the vehicle is driving at a relatively high speed. One is that the camera will collect blurred photos when driving at high speed which may affect the accuracy of deep neural networks. The other is that small objects far away from the vehicle are difficult to be recognized by networks. In this paper, we present a meth

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Wang, Chen. "2D object detection and semantic segmentation in the Carla simulator." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291337.

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The subject of self-driving car technology has drawn growing interest in recent years. Many companies, such as Baidu and Tesla, have already introduced automatic driving techniques in their newest cars when driving in a specific area. However, there are still many challenges ahead toward fully autonomous driving cars. Tesla has caused several severe accidents when using autonomous driving functions, which makes the public doubt self-driving car technology. Therefore, it is necessary to use the simulator environment to help verify and perfect algorithms for the perception, planning, and decisio

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Ferrer, Bustamante Claudia Mariela, Llanos Víctor Hugo Ibarra, and Flores Carlos Rafael Prialé. "Plataforma virtual de Rifa Yolo." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2018. http://hdl.handle.net/10757/625450.

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El presente proyecto de negocio ha sido trabajado con la finalidad de atender la necesidad de las personas que son usuarias del comercio electrónico ofreciéndoles una manera innovadora de obtener productos por un costo mínimo. El objetivo de este plan es aproximar productos a los consumidores que tienen el deseo de tenerlos pero que por diversas razones no han podido conseguirlos. En esta propuesta elaborada para cumplir el deseo de nuestro cliente elegido, se ha trabajado en la identificación de sus principales motivaciones al momento de comprar por internet como son: el ahorro de tiempo

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Libros sobre el tema "YOLOv8"

Jones, Sam. Yolo. Simon Pulse, 2014.

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1564-1616, Shakespeare William, ed. YOLO Juliet. Random House, 2015.

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Oxlajuuj Keej Maya' Ajtz'iib' (Group). and Centro Educativo y Cultural Maya., eds. Jkemiik yoloj li uspanteko =: Gramática uspanteka. OKMA, 2007.

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O, Obemeata Joseph, Ayodele Samuel O, Araromi M. A, and Yoloye E. Ayotunde, eds. Evaluation in Africa: In honour of Professor E.A. Yoloye. Stirling-Horden Publishers, 1999.

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Academia de las Lenguas Mayas de Guatemala, ed. Yolooj chib' jb'iijaq aj Tz'unun Kaab' =: Nombres y apellidos uspantekos. Academia de Lenguas Mayas de Guatemala, 2003.

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Uspanteka, Comunidad Lingüística, ed. Yolooj chib' jb'iijaq aj Tz'unun Kaab': Nombres y apellidos uspantekos. Academia de Lenguas Mayas de Guatemala, 2003.

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Stevens, James L., and Rosenberg David. Judges of Yolo County: 1850-1985. [s.n.], 2011.

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Kärimova, Häqiqät. Şäräfli ömür yolo: Vagif Abbasov-50. Tähsil, 2002.

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California. Dept. of Water Resources. Central District., ed. Historical ground water levels in Yolo County. Dept. of Water Resources, Central District, 1992.

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Zentner & Zentner. Cache Creek environmental restoration program, Yolo County, California. Zentner & Zentner, 1993.

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Capítulos de libros sobre el tema "YOLOv8"

Bikienga, Moustapha, Manegaouindé Roland Tougma, Sanguirè Pascal Somda, and Boureima Zerbo. "Comparative Study of YOLOv8, YOLOv9 and YOLOv10 by Their Ability to Detect Mangoes." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0692-4_7.

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Thanh, Bui Dang, Mac Tuan Anh, Giap Dang Khanh, Trinh Cong Dong, and Nguyen Thanh Huong. "SGDR-YOLOv8: Training Method for Rice Diseases Detection Using YOLOv8." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70906-7_15.

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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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Alves, Adília, José Pereira, Salik Khanal, A. Jorge Morais, and Vitor Filipe. "Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53036-4_4.

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Taskin, Elif Melis. "Interactive Neural Network for Object Detection in YOLOv5 and YOLOv8." In Information Systems Engineering and Management. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69197-3_30.

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Chintala, Lakshmi Narayana, K. Sreerama Murthy, and Venkata Ramana Kondapalli. "Improving Performance of Plant Disease Detection Using YOLOv7 and YOLOv8." In Springer Proceedings in Mathematics & Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51338-1_5.

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Shan, Chun, Hongming Liu, Yu Yu, Chuixin Chen, Mianjie Li, and Tongyi Zou. "YOLOv8-MING: Research on Privacy Fall Detection Algorithm Based on Improved YOLOv8." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4506-0_26.

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Kapil, Bhavesh, and Kamlesh Dutta. "Fabric Defects Detection Using YOLOv8." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6992-6_30.

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Liu, Bojie, and Daming Liu. "Improved YOLOv8 Fall Detection Algorithm." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1071-6_9.

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Saeleung, Pattanapong, Piyanart Chotikawanid, Yutthapong Pianroj, Saysunee Jumrat, Teerask Punvichai, and Jirapond Muangprathub. "Optimizing YOLOv7 Versus YOLOv8: A Comparative Study on Fruit Detection Performance." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1747-0_33.

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Actas de conferencias sobre el tema "YOLOv8"

V, Juno Bella Gracia S., J. Godwin Ponsam, Yogeshwaran B, Jenish J, and Dharanish Baabu P S. "A Study on Fast Detection and Evaluating YOLOV5,YOLOV6,YOLOV7 and YOLOV8." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11020238.

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Ikmel, Ghita, and EL AMRANI EL IDRISSI Najiba. "Performance Analysis of YOLOv5, YOLOv7, YOLOv8, and YOLOv9 on Road Environment Object Detection: Comparative Study." In 2024 International Conference on Ubiquitous Networking (UNet). IEEE, 2024. https://doi.org/10.1109/unet62310.2024.10794724.

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Mohajeran, Seena, Hannah Ke, Jenna Ke, Michelle Li, Yu Bai, and Macy Li. "Streamlined Video Object Detection with YOLOX YOLOV5 YOLOV7 and YOLOV8." In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2024. http://dx.doi.org/10.1109/codit62066.2024.10708395.

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Luo, Zhiping, Yilai Zhang, and Chao Li. "A Comparative Study of YOLOv8, YOLOv9, and YOLOv10 in Tile Defect Detection." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872483.

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A’la, Fiddin Yusfida, Muhammad Asri Safi'ie, and Andy Supriyadi. "YOLOv8 vs. YOLOv9: Safety Helmet Detection Performance." In 2024 7th International Conference of Computer and Informatics Engineering (IC2IE). IEEE, 2024. http://dx.doi.org/10.1109/ic2ie63342.2024.10748076.

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Chhabra, Payal, Tanmay Arora, Vibhore Jain, Neeraj Gandhi, Swapnil Bhatnagar, and Nitin Arora. "VisiCompanion: Comparative Analysis of Yolov8 and Yolov9." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986528.

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Mulla, Sufiya, Rajlaxmi Mandavkar, Simran Jamadar, Sneha Magdum, and Uma Gurav. "Camouflaged Human Detection: Comparative Analysis Using YoloV5s, Yolov5l, Yolov5m, Yolov5n, Yolov5x Model." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870666.

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Subair, Rubiya, and Shajulin Benedict. "Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, and YOLOv9: A Study on Performance and Explainability." In 2024 IEEE Silchar Subsection Conference (SILCON). IEEE, 2024. https://doi.org/10.1109/silcon63976.2024.10910543.

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Constantinescu, Mihai, Bogdan Alexandrescu, and Rodica-Claudia Constantinescu. "YOLOV4 vs YOLOV8 Decisional Spaces Based Comparative Study." In 2024 Advanced Topics on Measurement and Simulation (ATOMS). IEEE, 2024. https://doi.org/10.1109/atoms60779.2024.10921530.

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Wu, Chi-Hsun, and David Shiung. "YOLOv8-WS: Improved YOLOv8n for Traffic Sign Detection." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10930095.

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Informes sobre el tema "YOLOv8"

Schoening, Timm. PyiFDOYOLO. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_2_2022.

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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping University Electronic Press, 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were test

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Yoo, Shinjae, Yonggang Cui, Ji Hwan Park, Yuewei Lin, and Yihui Ren. Development of a software tool for IAEA use of the YOLOv3 machine learning algorithm. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1494041.

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Cui, Yonggang, S. Yoo, and J. Hwan Park. YOLO Test Software v1.2. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1646872.

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Mohamed, Amna. Towards Machine Learning Framework for Badminton Game Analysis Using TrackNet and YOLO Models. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-1513.

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Speer, B. First Known Use of QECBs will Save Yolo County at Least $8.7 Million Over the Next 25 Years, Energy Analysis (Revised) (Brochure). Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1008195.

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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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Chemical quality of ground water in Yolo and Solano counties, California. US Geological Survey, 1985. http://dx.doi.org/10.3133/wri844244.

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Streamflow, sediment discharge, and streambank erosion in Cache Creek, Yolo County, California, 1953-86. US Geological Survey, 1989. http://dx.doi.org/10.3133/wri884188.

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