Academic literature on the topic 'You only look once v8'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'You only look once v8.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "You only look once v8"

1

Huangfu, Zhongmin, and Shuqing Li. "Lightweight You Only Look Once v8: An Upgraded You Only Look Once v8 Algorithm for Small Object Identification in Unmanned Aerial Vehicle Images." Applied Sciences 13, no. 22 (2023): 12369. http://dx.doi.org/10.3390/app132212369.

Full text
Abstract:
In order to solve the problems of high leakage rate, high false detection rate, low detection success rate and large model volume of small targets in the traditional target detection algorithm for Unmanned Aerial Vehicle (UAV) aerial images, a lightweight You Only Look Once (YOLO) v8 algorithm model Lightweight (LW)-YOLO v8 is proposed. By increasing the channel attention mechanism Squeeze-and-Excitation (SE) module, this method can adaptively improves the model’s ability to extract features from small targets; at the same time, the lightweight convolution technology is introduced into the Conv module, where the ordinary convolution is replaced by the GSConv module, which can effectively reduce the model computational volume; on the basis of the GSConv module, a single aggregation module VoV-GSCSPC is designed to optimize the model structure in order to achieve a higher computational cost-effectiveness. The experimental results show that the LW-YOLO v8 model’s mAP@0.5 metrics on the VisDrone2019 dataset are more favorable than those on the YOLO v8n model, improving by 3.8 percentage points, and the computational amount is reduced to 7.2 GFLOPs. The LW-YOLO v8 model proposed in this work can effectively accomplish the task of detecting small targets in aerial images from UAV at a lower cost.
APA, Harvard, Vancouver, ISO, and other styles
2

Rizqi Basuki, Nurfadjri Akbar, and Hustinawaty Hustinawaty. "You only look once v8 for fish species identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3314. http://dx.doi.org/10.11591/ijai.v13.i3.pp3314-3321.

Full text
Abstract:
<p>This research aims to test the performance of you only look once (YOLOv8) in identifying fish species in Indonesian waters. Fish image data is obtained from various sources to conduct testing. The results show that YOLOv8 is able to identify fish species with a mAP accuracy rate of 97%. These results reveal the great potential of deep learning technology in supporting the preservation of marine biodiversity as well as the development of various applications, such as fisheries monitoring, conservation, and marine-based tourism development in Indonesia. With its efficient object detection and classification capabilities, YOLOv8 can simplify and accelerate the process of identifying fish species, even on a large scale. Thus, this technology is a highly effective solution to overcome the challenges of manual fish species identification, which requires a lot of time and effort. The results of this study provide valuable insights into the potential utilization of Indonesia's natural resources in the context of scientific development, the tourism industry, and the fisheries sector, which is vital to the country's economy.</p>
APA, Harvard, Vancouver, ISO, and other styles
3

Nurfadjri, Akbar Rizqi Basuki, and Hustinawaty Hustinawaty. "You only look once v8 for fish species identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3314–21. https://doi.org/10.11591/ijai.v13.i3.pp3314-3321.

Full text
Abstract:
This research aims to test the performance of you only look once (YOLOv8) in identifying fish species in Indonesian waters. Fish image data is obtained from various sources to conduct testing. The results show that YOLOv8 is able to identify fish species with a mAP accuracy rate of 97%. These results reveal the great potential of deep learning technology in supporting the preservation of marine biodiversity as well as the development of various applications, such as fisheries monitoring, conservation, and marine-based tourism development in Indonesia. With its efficient object detection and classification capabilities, YOLOv8 can simplify and accelerate the process of identifying fish species, even on a large scale. Thus, this technology is a highly effective solution to overcome the challenges of manual fish species identification, which requires a lot of time and effort. The results of this study provide valuable insights into the potential utilization of Indonesia's natural resources in the context of scientific development, the tourism industry, and the fisheries sector, which is vital to the country's economy.
APA, Harvard, Vancouver, ISO, and other styles
4

Arifadilah, Daffa, Asriyanik, and Agung Pambudi. "Sunda Script Detection Using You Only Look Once Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 3, no. 2 (2024): 606–13. http://dx.doi.org/10.59934/jaiea.v3i2.443.

Full text
Abstract:
The Sundanese script is a writing system used in the Sundanese language, one of the regional languages of West Java, Indonesia. This study investigates the use of the YOLO v8 algorithm for the real-time video detection of Sundanese script. Various versions of YOLO v8, including YOLO v8n, v8s, v8m, v8l, and v8x, were tested to determine the most effective model. After a comprehensive evaluation involving the analysis of mean Average Precision (mAP), F1-Confidence, and precision, the study selected the YOLO v8s model as the primary detection method. YOLO v8s demonstrated superior performance with the highest mAP of 98.835%, an F1-Confidence of 98%, and a precision of 76,2%. This choice was based on a balance between high accuracy and computational efficiency. The results indicate significant potential for object recognition technology in the learning and preservation of Sundanese script.
APA, Harvard, Vancouver, ISO, and other styles
5

Priandini, Jesita Reinandra. "Pengenalan Rambu Lalu Lintas Menggunakan Model You Only Look Once (YOLO) V8." Jurnal Rekayasa Sistem Informasi dan Teknologi 2, no. 2 (2024): 801–9. https://doi.org/10.70248/jrsit.v2i2.1607.

Full text
Abstract:
Mobil autonomous adalah kendaraan yang memiliki kemampuan untuk berjalan secara mandiri tanpa bantuan manusia. Walau bagaimanapun, mobil ini memiliki masalah dalam mendeteksi rambu lalu lintas. Pengenal rambu lalu lintas dirancang untuk membuat mobil autonomous lebih aman karena mereka dapat mengenali rambu lalu lintas yang dilewati. Metode ini menggunakan model YOLOv8, pengembangan dari metode Convolutional Neural Network, untuk mendeteksi dan mengklasikasi rambu lalu lintas. Model ini dipilih karena sangat efisiensi dan akurat. Dataset Roboflow yang berisi 2390 gambar dari 17 jenis rambu lalu lintas Indonesia digunakan dalam penelitian ini. Dengan nilai akurasi sebesar 97,90%, nilai ketepatan sebesar 0,978, nilai recall sebesar 0,989, nilai MAP50 sebesar 0.987, dan nilai MAP50- 95 sebesar 0,825, penelitian ini menunjukkan bahwa model ini bekerja dengan sangat baik. Nilai ini menunjukkan bahwa model dapat dengan akurat menemukan dan mengklasifikasikan rambu lalu lintas.
APA, Harvard, Vancouver, ISO, and other styles
6

Hayati, Nurhaliza Juliyani, Dayan Singasatia, and Muhamad Rafi Muttaqin. "Object Tracking Menggunakan Algoritma You Only Look Once (YOLO)v8 untuk Menghitung Kendaraan." Komputa : Jurnal Ilmiah Komputer dan Informatika 12, no. 2 (2023): 91–99. http://dx.doi.org/10.34010/komputa.v12i2.10654.

Full text
Abstract:
Vehicles are a means of transportation that have existed from ancient times until now, many people use vehicles such as cars and motorbikes. Enumeration of types and numbers of vehicles is carried out to collect traffic data information. In obtaining data parameters for the number of vehicles, manual calculations are usually prone to errors and take a lot of time and energy. The application of Artificial Intelligence such as object detection is a field of computer vision. In intelligent transportation systems, traffic data is the key to conducting research and designing a system. To overcome the problem, researchers carried out object tracking using the You Only Look Once (YOLO) v8 algorithm to detect the type and count the number of vehicles. The methodology applied is the AI Project Cycle stages which use problem scoping, data acquisition, data exploration, modeling, and confusion matrix evaluation. The results of the confusion matrix evaluation obtained an accuracy level of 89%, precision of 89%, recall of 90% and a weighted comparison of precision and recall obtained an F1-Score value of 89%. Thus, the You Only Look Once (YOLO) v8 algorithm is accurate enough to detect object tracking to calculate vehicles.
APA, Harvard, Vancouver, ISO, and other styles
7

Akyas Hifdzi Rahman, Rifqi, Asril Adi Sunarto, and Asriyanik Asriyanik. "PENERAPAN YOU ONLY LOOK ONCE (YOLO) V8 UNTUK DETEKSI TINGKAT KEMATANGAN BUAH MANGGIS." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 5 (2024): 10566–71. http://dx.doi.org/10.36040/jati.v8i5.10979.

Full text
Abstract:
Indonesia memiliki potensi besar dalam produksi buah-buahan tropis, salah satunya adalah manggis (Garcinia mangostana Linn) yang dikenal sebagai "ratu buah". Namun, proses klasifikasi kematangan manggis masih dilakukan secara manual, yang rentan terhadap kesalahan manusia. Penelitian ini bertujuan mengembangkan model deteksi kematangan buah manggis menggunakan Algoritma You Only Look Once (YOLO) untuk meningkatkan akurasi dan efisiensi penyortiran. Dengan menggunakan pendekatan CRISP-DM, data gambar manggis dikumpulkan dan diproses untuk dilabeli dan di-augmentasi. Hasil penelitian menunjukkan bahwa model YOLOv8s dengan optimizer SGD memberikan hasil terbaik dengan precision 0.997, recall 1, dan mAP50-95 sebesar 0.972. Implementasi model ini ke dalam sistem berbasis web menunjukkan potensi besar dalam menggantikan metode manual yang rentan terhadap kesalahan manusia. Model ini diharapkan dapat meningkatkan efisiensi dan akurasi dalam industri pertanian, khususnya untuk penyortiran buah manggis.
APA, Harvard, Vancouver, ISO, and other styles
8

Afiansyah, Rifan, Prajoko Prajoko, and Asriyanik Asriyanik. "PEMODELAN DETEKSI BELA DIRI BERBASIS WEB DENGAN ALGORITMA YOU ONLY LOOK ONCE V8." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 5 (2024): 9970–77. http://dx.doi.org/10.36040/jati.v8i5.10879.

Full text
Abstract:
Seni bela diri merupakan aktivitas yang tidak hanya berfungsi sebagai metode pertahanan diri, tetapi juga memiliki manfaat positif seperti menjaga kesehatan, meningkatkan disiplin, dan mempromosikan nilai-nilai budaya. Dengan minat yang semakin meningkat terhadap teknologi deteksi gerakan bela diri untuk tujuan pelatihan dan edukasi, penelitian sebelumnya telah menggunakan berbagai metode seperti Convolutional Neural Network (CNN) untuk mendeteksi gerakan silat dengan akurasi 77%, serta Support Vector Machine dan YOLOv3 untuk klasifikasi pose dasar karate dengan hasil presisi, recall, dan F1 Score yang tinggi, meskipun masih terdapat tingkat kesalahan sebesar 66,66%. Namun, penelitian-penelitian tersebut umumnya terbatas pada deteksi satu jenis bela diri. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem deteksi gerakan bela diri berbasis web menggunakan metode YOLOv8, dengan fokus pada tiga jenis bela diri: karate, taekwondo, dan silat. Metode YOLO dipilih karena kemampuannya dalam mendeteksi objek secara real-time, dengan memprediksi kotak pembatas dan probabilitas kelas langsung pada satu gambar penuh dalam satu evaluasi. Model yang dikembangkan diharapkan dapat mengenali gerakan dengan tingkat akurasi minimal 90% dan mencakup 25 kelas gerakan yang meliputi ketiga jenis bela diri tersebut. Model ini dilatih secara real-time menggunakan data yang diolah dan di-augmentasi melalui Roboflow, serta menggunakan optimizer AdamW dengan learning rate 0.001. Pengujian dengan 50 epoch menunjukkan akurasi tinggi, dengan metrik precision, recall, dan F1 yang hampir sempurna. Model ini kemudian diimplementasikan dalam sebuah website sederhana yang memungkinkan pengguna mendeteksi gerakan bela diri secara interaktif, menunjukkan potensi besar dalam aplikasi praktis di masa depan. Penelitian ini diharapkan dapat mendorong pengembangan teknologi deteksi gerakan dalam seni bela diri dan memberikan kontribusi terhadap peningkatan kualitas latihan serta edukasi seni bela diri.
APA, Harvard, Vancouver, ISO, and other styles
9

Bayu Pangestu, Andhika, Muhamad Rafi Muttaqin, and Muhamad Agus Sunandar. "SISTEM DETEKSI BAHASA ISYARAT INDONESIA (BISINDO) MENGGUNAKAN ALGORITMA YOU ONLY LOOK ONCE (YOLO)v8." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 5 (2024): 9891–97. http://dx.doi.org/10.36040/jati.v8i5.10833.

Full text
Abstract:
Bahasa Isyarat Indonesia (BISINDO) adalah alat komunikasi yang penting bagi penyandang tunarungu di Indonesia, namun banyak orang dengan kemampuan mendengar yang belum memahaminya. Untuk memfasilitasi komunikasi, penelitian ini merancang sistem deteksi BISINDO menggunakan algoritma YOLOv8. Algoritma YOLOv8 dilatih dengan dataset gambar dan vidio yang telah diklasifikasikan, dan sistem ini diimplementasikan menggunakan platform Streamlit untuk aksesibilitas yang mudah. Data digunakan untuk melatih dan menguji model dalam berbagai kondisi pencahayaan dan latar belakang. Hasil evaluasi menunjukkan nilai precision sebesar 0.958 (95.8%), recall sebesar 0.974 (97.4%), dan mAP50 mencapai 0.995 (99.5%). Sementara itu, nilai mAP50-90 adalah 0.884 (88.4%), dengan waktu pemrosesan selama 1 jam. Evaluasi menggunakan confusion matrix dan Mean Average Precision (mAP) menunjukkan bahwa model memiliki kinerja yang baik dalam mendeteksi objek. Implementasi ini efektif dalam mengatasi hambatan komunikasi antara penyandang tunarungu dan masyarakat umum, mendukung pembangunan inklusif di Indonesia.
APA, Harvard, Vancouver, ISO, and other styles
10

Ekhsanto, Bagus kurniawan, Bagus Adhi Kusuma, and Adam Prayogo Kuncoro. "IMPLEMENTATION OF YOU ONLY LOOK ONCE V8 ALGORITHM IN POTATO LEAF DISEASE DETECTION SYSTEM." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 125–32. https://doi.org/10.52436/1.jutif.2024.5.4.2104.

Full text
Abstract:
Agriculture is an important foundation of the national economy, as effective development in this sector will support overall economic stability. Potato itself is one of the world's staple foods after rice, wheat and corn. This crop belongs to the category of horticulture which is widely planted and developed by people to meet their needs. On the farm of Bibit sida kangen Kalibening, Banjarnegara which is one of the farms that grow potatoes has constraints related to potato diseases which result in decreased productivity of crops. Therefore, the main purpose of this system is to provide fast and accurate disease detection capability on the farm of Bibit sida kangen Kalibening, Banjarnegara, so that it can help farmers in reducing losses caused by disease attacks on plants. By utilizing YOU ONLY LOOK ONCE V8 (YOLOv8) technology, this system can recognize and classify potato leaf disease types, including early_blight, late_blight, and healthy plants, with a high level of accuracy. Through evaluation using precision and recall matrices, the results show a significant success rate, with precision accuracy for early_blight of 87%, healthy plants of 81%, and late_blight of 97%, respectively. Meanwhile, the recall results for the three categories also reached 87%, 81%, and 97% respectively. With an overall accuracy of 88%, these findings confirm that the developed detection system is successful in identifying potato leaf diseases with high accuracy. This indicates the great potential of this system in assisting farmers in managing the condition of their potato crops, which in turn can improve farmers' productivity and welfare.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "You only look once v8"

1

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.

Full text
Abstract:
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 examine if they can successfully be used to extract information in environments such as construction, forestry and mining. The amount of data needed to successfully train a real time object detector was also investigated. Furthermore, the thesis examines if there are specifically difficult situations for the defined object detector, how reliable the object detector is and finally how to use service-oriented architecture principles can be used to create deep learning systems. To investigate the questions formulated above, three data sets were created where different properties were varied. These properties were light conditions, ground material and dump truck orientation. The data sets were created using a toy dump truck together with a similarly sized wheel loader with a camera mounted on the roof of its cab. The first data set contained only indoor images where the dump truck was placed in different orientations but neither the light nor the ground material changed. The second data set contained images were the light source was kept constant, but the dump truck orientation and ground materials changed. The last data set contained images where all property were varied. The real time object detector YOLOv3 was used to examine how a real time object detector would perform depending on which one of the three data sets it was trained using. No matter the data set, it was possible to train a model to perform real time object detection. Using a Nvidia 980 TI the inference time of the model was around 22 ms, which is more than enough to be able to classify videos running at 30 fps. All three data sets converged to a training loss of around 0.10. The data set which contained more varied data, such as the data set where all properties were changed, performed considerably better reaching a validation loss of 0.164 compared to the indoor data set, containing the least varied data, only reached a validation loss of 0.257. The size of the data set was also a factor in the performance, however it was not as important as having varied data. The result also showed that all three data sets could reach a mAP score of around 0.98 using transfer learning.
APA, Harvard, Vancouver, ISO, and other styles
2

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/.

Full text
Abstract:
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 for reaching impressive processing speeds, but its intrinsic structure makes it struggle in detecting small objects clustered together, which unfortunately matches our scenario: we are trying to read alphanumerical codes by detecting each single character and then reconstructing the final string. The final goal of this thesis is to overcome this drawback and push the accuracy performances of a general object detector convolutional neural network to its limits, in order to meet the demanding requirements of industrial OCR applications. To accomplish this, first YOLO's unique detecting approach was mastered in its original framework called Darknet, written in C and CUDA, then all the code was translated into Python programming language for a better flexibility, which also allowed the deployment of a custom architecture. Four different datasets with increasing complexity were used as case-studies and the final performances reached were surprising: the accuracy varies between 99.75\% and 99.97\% with a processing time of 15 ms for images $1000\times1000$ big, largely outperforming in speed the current deep learning solution deployed by Datalogic. On the downsides, the training phase usually requires a very large amount of data and time and YOLO also showed some memorization behaviours if not enough variability is given at training time.
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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 without interfering with vehicles or humans ground. In this thesis, we solve the problem of using UAV on autonomous sites, finding an object and critical situation, support autonomous site operators with an extra safety layer from UAVs camera. After finding an object on such a site, uses GPS-coordinates from the UAV to see and place the detected object on the site onto a gridmap, leaving a coordinate-map to the operator to see where the objects are and see if the critical situation can occur. Directly under the object detection, reporting critical situations can be done because of safety-distance-circle leaving warnings if objects come to close to each other. However, the system itself only supports the operator with extra safety and warnings, leaving the operator with the choice of pressing emergency stop or not. Object detection uses You only look once (YOLO) as main object detection Neural Network (NN), mixed with edge-detection for gaining accuracy during bird-eye-views and motion-detection for supporting finding all object that is moving on-site, even if UAV cannot find all the objects on site. Result proofs that the UAV-surveillance on autonomous site is an excellent way to add extra safety on-site if the operator is out of focus or finding objects on-site before startup since the operator can fly the UAV around the site, leaving an extra-safety-layer of finding humans on-site before startup. Also, moving the UAV to a specific position, where extra safety is needed, informing the operator to limit autonomous vehicles speed around that area because of humans operation on site. The use of single object detection limits the effects but gathered object detection methods lead to a promising result while printing those objects onto a global positions system (GPS) map has proposed a new field to study. It leaves the operator with a viewable interface outside of object detection libraries.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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 spela in trafiksituationer och en dator med Linux som operativsystem. Med hjälp av metoder som vanligen används för att skapa ett automatiserat trafikledningssystem utvärderades ett målföljningssystem. De slutliga resultaten visar att systemet är relativt instabilt och ibland inte kan känna igen vissa objekt. Om fler bilder används för träningsprocessen kan ett robustare och mycket mer tillförlitligt system utvecklas med liknande metodik.<br>Autonomous vehicles can decrease traffic congestion and reduce the amount of traffic related accidents. As there will be millions of autonomous vehicles in the future, a better understanding of the environment will be required. This project aims to create an external automated traffic system that can detect and track 3D objects within a complex traffic situation to later send these objects’ behavior for a larger-scale project that manages to 3D model the traffic situation. The project utilizes Tensorflow framework and YOLOv3 algorithm. The project also utilizes a camera to record traffic situations and a Linux operated computer. Using methods commonly used to create an automated traffic management system was evaluated. The final results show that the system is relatively unstable and can sometimes fail to recognize certain objects. If more images are used for the training process, a more robust and much more reliable system could be developed using a similar methodology.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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 conventional with different types of Convolutional Neural Networks. A segmentation network was used as well as a few detection networks and different fusions between these. The Convolutional Neural Network approach proved to be the better of the two in terms of precision and recall but the clustering approach was not far behind. This work was done using a relatively small amount of data which potentially could have impacted the results of the Machine Learning models in a negative way.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "You only look once v8"

1

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

Full text
Abstract:
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 thing on Earth. It often compounds upon itself, so delays in reducing emissions of fossil fuels are shortening the amount of time remaining to eliminate the use of fossil fuels to preserve a livable planet. Nationalism often impedes solutions to this problem (among many others), as nations place their singular needs above the common good. Our initial proposal got around, and abstracts on many subjects arrived. Within a few weeks, we had enough good material for a 100,000-word book. The book then fattened to two moderate volumes and then to four two very hefty tomes. We tried several different titles as good submissions swelled. We also discovered that our best contributors were experts in their fields, which ranged the world. We settled on three stand-alone books:” 1/ nationalism and racial justice. Our first volume grew as the growth of Black Lives Matter following the brutal killing of George Floyd ignited protests over police brutality and other issues during 2020, following the police assassination of Floyd in Minneapolis. It is estimated that more people took part in protests of police brutality during the summer of 2020 than any other series of marches in United States history. This includes upheavals during the 1960s over racial issues and against the war in Southeast Asia (notably Vietnam). We choose a volume on racism because it is one of nationalism’s main motive forces. This volume provides a worldwide array of work on nationalism’s growth in various countries, usually by authors residing in them, or in the United States with ethnic ties to the nation being examined, often recent immigrants to the United States from them. Our roster of contributors comprises a small United Nations of insightful, well-written research and commentary from Indonesia, New Zealand, Australia, China, India, South Africa, France, Portugal, Estonia, Hungary, Russia, Poland, Kazakhstan, Georgia, and the United States. Volume 2 (this one) describes and analyzes nationalism, by country, around the world, except for the United States; and 3/material directly related to President Donald Trump, and the United States. The first volume is under consideration at the Texas A &amp; M University Press. The other two are under contract to Nova Science Publishers (which includes social sciences). These three volumes may be used individually or as a set. Environmental material is taken up in appropriate places in each of the three books. * * * * * What became the United States of America has been strongly nationalist since the English of present-day Massachusetts and Jamestown first hit North America’s eastern shores. The country propelled itself across North America with the self-serving ideology of “manifest destiny” for four centuries before Donald Trump came along. Anyone who believes that a Trumpian affection for deportation of “illegals” is a new thing ought to take a look at immigration and deportation statistics in Adam Goodman’s The Deportation Machine: America’s Long History of Deporting Immigrants (Princeton University Press, 2020). Between 1920 and 2018, the United States deported 56.3 million people, compared with 51.7 million who were granted legal immigration status during the same dates. Nearly nine of ten deportees were Mexican (Nolan, 2020, 83). This kind of nationalism, has become an assassin of democracy as well as an impediment to solving global problems. Paul Krugman wrote in the New York Times (2019:A-25): that “In their 2018 book, How Democracies Die, the political scientists Steven Levitsky and Daniel Ziblatt documented how this process has played out in many countries, from Vladimir Putin’s Russia, to Recep Erdogan’s Turkey, to Viktor Orban’s Hungary. Add to these India’s Narendra Modi, China’s Xi Jinping, and the United States’ Donald Trump, among others. Bit by bit, the guardrails of democracy have been torn down, as institutions meant to serve the public became tools of ruling parties and self-serving ideologies, weaponized to punish and intimidate opposition parties’ opponents. On paper, these countries are still democracies; in practice, they have become one-party regimes….And it’s happening here [the United States] as we speak. If you are not worried about the future of American democracy, you aren’t paying attention” (Krugmam, 2019, A-25). We are reminded continuously that the late Carl Sagan, one of our most insightful scientific public intellectuals, had an interesting theory about highly developed civilizations. Given the number of stars and planets that must exist in the vast reaches of the universe, he said, there must be other highly developed and organized forms of life. Distance may keep us from making physical contact, but Sagan said that another reason we may never be on speaking terms with another intelligent race is (judging from our own example) could be their penchant for destroying themselves in relatively short order after reaching technological complexity. This book’s chapters, introduction, and conclusion examine the worldwide rise of partisan nationalism and the damage it has wrought on the worldwide pursuit of solutions for issues requiring worldwide scope, such scientific co-operation public health and others, mixing analysis of both. We use both historical description and analysis. This analysis concludes with a description of why we must avoid the isolating nature of nationalism that isolates people and encourages separation if we are to deal with issues of world-wide concern, and to maintain a sustainable, survivable Earth, placing the dominant political movement of our time against the Earth’s existential crises. Our contributors, all experts in their fields, each have assumed responsibility for a country, or two if they are related. This work entwines themes of worldwide concern with the political growth of nationalism because leaders with such a worldview are disinclined to co-operate internationally at a time when nations must find ways to solve common problems, such as the climate crisis. Inability to cooperate at this stage may doom everyone, eventually, to an overheated, stormy future plagued by droughts and deluges portending shortages of food and other essential commodities, meanwhile destroying large coastal urban areas because of rising sea levels. Future historians may look back at our time and wonder why as well as how our world succumbed to isolating nationalism at a time when time was so short for cooperative intervention which is crucial for survival of a sustainable earth. Pride in language and culture is salubrious to individuals’ sense of history and identity. Excess nationalism that prevents international co-operation on harmful worldwide maladies is quite another. As Pope Francis has pointed out: For all of our connectivity due to expansion of social media, ability to communicate can breed contempt as well as mutual trust. “For all our hyper-connectivity,” said Francis, “We witnessed a fragmentation that made it more difficult to resolve problems that affect us all” (Horowitz, 2020, A-12). The pope’s encyclical, titled “Brothers All,” also said: “The forces of myopic, extremist, resentful, and aggressive nationalism are on the rise.” The pope’s document also advocates support for migrants, as well as resistance to nationalist and tribal populism. Francis broadened his critique to the role of market capitalism, as well as nationalism has failed the peoples of the world when they need co-operation and solidarity in the face of the world-wide corona virus pandemic. Humankind needs to unite into “a new sense of the human family [Fratelli Tutti, “Brothers All”], that rejects war at all costs” (Pope, 2020, 6-A). Our journey takes us first to Russia, with the able eye and honed expertise of Richard D. Anderson, Jr. who teaches as UCLA and publishes on the subject of his chapter: “Putin, Russian identity, and Russia’s conduct at home and abroad.” Readers should find Dr. Anderson’s analysis fascinating because Vladimir Putin, the singular leader of Russian foreign and domestic policy these days (and perhaps for the rest of his life, given how malleable Russia’s Constitution has become) may be a short man physically, but has high ambitions. One of these involves restoring the old Russian (and Soviet) empire, which would involve re-subjugating a number of nations that broke off as the old order dissolved about 30 years ago. President (shall we say czar?) Putin also has international ambitions, notably by destabilizing the United States, where election meddling has become a specialty. The sight of Putin and U.S. president Donald Trump, two very rich men (Putin $70-$200 billion; Trump $2.5 billion), nuzzling in friendship would probably set Thomas Jefferson and Vladimir Lenin spinning in their graves. The road of history can take some unanticipated twists and turns. Consider Poland, from which we have an expert native analysis in chapter 2, Bartosz Hlebowicz, who is a Polish anthropologist and journalist. His piece is titled “Lawless and Unjust: How to Quickly Make Your Own Country a Puppet State Run by a Group of Hoodlums – the Hopeless Case of Poland (2015–2020).” When I visited Poland to teach and lecture twice between 2006 and 2008, most people seemed to be walking on air induced by freedom to conduct their own affairs to an unusual degree for a state usually squeezed between nationalists in Germany and Russia. What did the Poles then do in a couple of decades? Read Hlebowicz’ chapter and decide. It certainly isn’t soft-bellied liberalism. In Chapter 3, with Bruce E. Johansen, we visit China’s western provinces, the lands of Tibet as well as the Uighurs and other Muslims in the Xinjiang region, who would most assuredly resent being characterized as being possessed by the Chinese of the Han to the east. As a student of Native American history, I had never before thought of the Tibetans and Uighurs as Native peoples struggling against the Independence-minded peoples of a land that is called an adjunct of China on most of our maps. The random act of sitting next to a young woman on an Air India flight out of Hyderabad, bound for New Delhi taught me that the Tibetans had something to share with the Lakota, the Iroquois, and hundreds of other Native American states and nations in North America. Active resistance to Chinese rule lasted into the mid-nineteenth century, and continues today in a subversive manner, even in song, as I learned in 2018 when I acted as a foreign adjudicator on a Ph.D. dissertation by a Tibetan student at the University of Madras (in what is now in a city called Chennai), in southwestern India on resistance in song during Tibet’s recent history. Tibet is one of very few places on Earth where a young dissident can get shot to death for singing a song that troubles China’s Quest for Lebensraum. The situation in Xinjiang region, where close to a million Muslims have been interned in “reeducation” camps surrounded with brick walls and barbed wire. They sing, too. Come with us and hear the music. Back to Europe now, in Chapter 4, to Portugal and Spain, we find a break in the general pattern of nationalism. Portugal has been more progressive governmentally than most. Spain varies from a liberal majority to military coups, a pattern which has been exported to Latin America. A situation such as this can make use of the term “populism” problematic, because general usage in our time usually ties the word into a right-wing connotative straightjacket. “Populism” can be used to describe progressive (left-wing) insurgencies as well. José Pinto, who is native to Portugal and also researches and writes in Spanish as well as English, in “Populism in Portugal and Spain: a Real Neighbourhood?” provides insight into these historical paradoxes. Hungary shares some historical inclinations with Poland (above). Both emerged from Soviet dominance in an air of developing freedom and multicultural diversity after the Berlin Wall fell and the Soviet Union collapsed. Then, gradually at first, right wing-forces began to tighten up, stripping structures supporting popular freedom, from the courts, mass media, and other institutions. In Chapter 5, Bernard Tamas, in “From Youth Movement to Right-Liberal Wing Authoritarianism: The Rise of Fidesz and the Decline of Hungarian Democracy” puts the renewed growth of political and social repression into a context of worldwide nationalism. Tamas, an associate professor of political science at Valdosta State University, has been a postdoctoral fellow at Harvard University and a Fulbright scholar at the Central European University in Budapest, Hungary. His books include From Dissident to Party Politics: The Struggle for Democracy in Post-Communist Hungary (2007). Bear in mind that not everyone shares Orbán’s vision of what will make this nation great, again. On graffiti-covered walls in Budapest, Runes (traditional Hungarian script) has been found that read “Orbán is a motherfucker” (Mikanowski, 2019, 58). Also in Europe, in Chapter 6, Professor Ronan Le Coadic, of the University of Rennes, Rennes, France, in “Is There a Revival of French Nationalism?” Stating this title in the form of a question is quite appropriate because France’s nationalistic shift has built and ebbed several times during the last few decades. For a time after 2000, it came close to assuming the role of a substantial minority, only to ebb after that. In 2017, the candidate of the National Front reached the second round of the French presidential election. This was the second time this nationalist party reached the second round of the presidential election in the history of the Fifth Republic. In 2002, however, Jean-Marie Le Pen had only obtained 17.79% of the votes, while fifteen years later his daughter, Marine Le Pen, almost doubled her father's record, reaching 33.90% of the votes cast. Moreover, in the 2019 European elections, re-named Rassemblement National obtained the largest number of votes of all French political formations and can therefore boast of being "the leading party in France.” The brutality of oppressive nationalism may be expressed in personal relationships, such as child abuse. While Indonesia and Aotearoa [the Maoris’ name for New Zealand] hold very different ranks in the United Nations Human Development Programme assessments, where Indonesia is classified as a medium development country and Aotearoa New Zealand as a very high development country. In Chapter 7, “Domestic Violence Against Women in Indonesia and Aotearoa New Zealand: Making Sense of Differences and Similarities” co-authors, in Chapter 8, Mandy Morgan and Dr. Elli N. Hayati, from New Zealand and Indonesia respectively, found that despite their socio-economic differences, one in three women in each country experience physical or sexual intimate partner violence over their lifetime. In this chapter ther authors aim to deepen understandings of domestic violence through discussion of the socio-economic and demographic characteristics of theit countries to address domestic violence alongside studies of women’s attitudes to gender norms and experiences of intimate partner violence. One of the most surprising and upsetting scholarly journeys that a North American student may take involves Adolf Hitler’s comments on oppression of American Indians and Blacks as he imagined the construction of the Nazi state, a genesis of nationalism that is all but unknown in the United States of America, traced in this volume (Chapter 8) by co-editor Johansen. Beginning in Mein Kampf, during the 1920s, Hitler explicitly used the westward expansion of the United States across North America as a model and justification for Nazi conquest and anticipated colonization by Germans of what the Nazis called the “wild East” – the Slavic nations of Poland, the Baltic states, Ukraine, and Russia, most of which were under control of the Soviet Union. The Volga River (in Russia) was styled by Hitler as the Germans’ Mississippi, and covered wagons were readied for the German “manifest destiny” of imprisoning, eradicating, and replacing peoples the Nazis deemed inferior, all with direct references to events in North America during the previous century. At the same time, with no sense of contradiction, the Nazis partook of a long-standing German romanticism of Native Americans. One of Goebbels’ less propitious schemes was to confer honorary Aryan status on Native American tribes, in the hope that they would rise up against their oppressors. U.S. racial attitudes were “evidence [to the Nazis] that America was evolving in the right direction, despite its specious rhetoric about equality.” Ming Xie, originally from Beijing, in the People’s Republic of China, in Chapter 9, “News Coverage and Public Perceptions of the Social Credit System in China,” writes that The State Council of China in 2014 announced “that a nationwide social credit system would be established” in China. “Under this system, individuals, private companies, social organizations, and governmental agencies are assigned a score which will be calculated based on their trustworthiness and daily actions such as transaction history, professional conduct, obedience to law, corruption, tax evasion, and academic plagiarism.” The “nationalism” in this case is that of the state over the individual. China has 1.4 billion people; this system takes their measure for the purpose of state control. Once fully operational, control will be more subtle. People who are subject to it, through modern technology (most often smart phones) will prompt many people to self-censor. Orwell, modernized, might write: “Your smart phone is watching you.” Ming Xie holds two Ph.Ds, one in Public Administration from University of Nebraska at Omaha and another in Cultural Anthropology from the Chinese Academy of Social Sciences, Beijing, where she also worked for more than 10 years at a national think tank in the same institution. While there she summarized news from non-Chinese sources for senior members of the Chinese Communist Party. Ming is presently an assistant professor at the Department of Political Science and Criminal Justice, West Texas A&amp;M University. In Chapter 10, analyzing native peoples and nationhood, Barbara Alice Mann, Professor of Honours at the University of Toledo, in “Divide, et Impera: The Self-Genocide Game” details ways in which European-American invaders deprive the conquered of their sense of nationhood as part of a subjugation system that amounts to genocide, rubbing out their languages and cultures -- and ultimately forcing the native peoples to assimilate on their own, for survival in a culture that is foreign to them. Mann is one of Native American Studies’ most acute critics of conquests’ contradictions, and an author who retrieves Native history with a powerful sense of voice and purpose, having authored roughly a dozen books and numerous book chapters, among many other works, who has traveled around the world lecturing and publishing on many subjects. Nalanda Roy and S. Mae Pedron in Chapter 11, “Understanding the Face of Humanity: The Rohingya Genocide.” describe one of the largest forced migrations in the history of the human race, the removal of 700,000 to 800,000 Muslims from Buddhist Myanmar to Bangladesh, which itself is already one of the most crowded and impoverished nations on Earth. With about 150 million people packed into an area the size of Nebraska and Iowa (population less than a tenth that of Bangladesh, a country that is losing land steadily to rising sea levels and erosion of the Ganges river delta. The Rohingyas’ refugee camp has been squeezed onto a gigantic, eroding, muddy slope that contains nearly no vegetation. However, Bangladesh is majority Muslim, so while the Rohingya may starve, they won’t be shot to death by marauding armies. Both authors of this exquisite (and excruciating) account teach at Georgia Southern University in Savannah, Georgia, Roy as an associate professor of International Studies and Asian politics, and Pedron as a graduate student; Roy originally hails from very eastern India, close to both Myanmar and Bangladesh, so he has special insight into the context of one of the most brutal genocides of our time, or any other. This is our case describing the problems that nationalism has and will pose for the sustainability of the Earth as our little blue-and-green orb becomes more crowded over time. The old ways, in which national arguments often end in devastating wars, are obsolete, given that the Earth and all the people, plants, and other animals that it sustains are faced with the existential threat of a climate crisis that within two centuries, more or less, will flood large parts of coastal cities, and endanger many species of plants and animals. To survive, we must listen to the Earth, and observe her travails, because they are increasingly our own.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "You only look once v8"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Smith, William A. P., and Toby Pillatt. "You Only Look for a Symbol Once: An Object Detector for Symbols and Regions in Documents." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-41734-4_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "You only look once v8"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "You only look once v8"

1

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

Full text
Abstract:
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 Pothole Management Program (PMP), as it enhances the program's efficiency, effectiveness, and user experience. The program utilizes two machine learning models. The first model, Visual Geometry Group (VGG16), uses deep learning neural network technology to classify potholes with over 90% accuracy. The second machine learning model, You Only Look Once (YOLO), has been designed to detect and accurately mark potholes on submitted photos. Overall, this innovative pothole management program offers a comprehensive solution to help address the critical issue of potholes in urban areas.
APA, Harvard, Vancouver, ISO, and other styles
2

Forero Fuarez, Luis Carlos. Procesamiento de imágenes. Escuela Tecnológica Instituto Técnico Central - ETITC, 2023. http://dx.doi.org/10.55411/2023.4.

Full text
Abstract:
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 reconocer las placas de los vehículos que ingresan a la ETITC en tiempo real y/o aplicaciones en general, como procesos de regresión, clasificación, segmentación, etc. Considerando que a futuro se planteará el trabajar con imágenes, se sabe que este campo presenta gran auge en distintos campos, pues como lo menciona LeCun et al. 2015, el uso de redes convolucionales ha ampliado la capacidad para extraer características relevantes de las imágenes, lo que es fundamental para el reconocimiento de placas de vehículos. Adicionalmente, se han desarrollado métodos como el introducido por Redmon J et al. (2016), el cual es conocido actualmente como YOLO "You Only Look Once" que mediante redes convolucionales facilita el reconocimiento de objetos. Adicionalmente se tiene el ejemplo de Krizhevsky, A (2012), quien mediante el modelo AlexNet, presentó gran eficacia en tareas de reconocimiento de imágenes.
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!