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Journal articles on the topic 'You only look once v8-seg'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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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.

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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 Con

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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.

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<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 detecti

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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.

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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 cl

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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.

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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 wit

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Hanna, Arini Parhusip, Trihandaru Suryasatriya, Indrajaya Denny, and Labadin Jane. "Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3291–305. https://doi.org/10.11591/ijai.v13.i3.pp3291-3305.

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You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The small number

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Sampurno, Rizky Mulya, Zifu Liu, R. M. Rasika D. Abeyrathna, and Tofael Ahamed. "Intrarow Uncut Weed Detection Using You-Only-Look-Once Instance Segmentation for Orchard Plantations." Sensors 24, no. 3 (2024): 893. http://dx.doi.org/10.3390/s24030893.

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Mechanical weed management is a drudging task that requires manpower and has risks when conducted within rows of orchards. However, intrarow weeding must still be conducted by manual labor due to the restricted movements of riding mowers within the rows of orchards due to their confined structures with nets and poles. However, autonomous robotic weeders still face challenges identifying uncut weeds due to the obstruction of Global Navigation Satellite System (GNSS) signals caused by poles and tree canopies. A properly designed intelligent vision system would have the potential to achieve the d

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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.

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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 lal

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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.

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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 t

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Arini Parhusip, Hanna, Suryasatriya Trihandaru, Denny Indrajaya, and Jane Labadin. "Implementation of YOLOv8-seg on store products to speed up the scanning process at point of sales." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3291. http://dx.doi.org/10.11591/ijai.v13.i3.pp3291-3305.

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<p>You only look once v8 (YOLOv8)-seg and its variants are implemented to accelerate the collection of goods for a store for selling activity in Indonesia. The method used here is object detection and segmentation of these objects, a combination of detection and segmentation called instance segmentation. The novelty lies in the customization and optimization of YOLOv8-seg for detecting and segmenting 30 specific Indonesian products. The use of augmented data (125 images augmented into 1,250 images) enhances the model's ability to generalize and perform well in various scenarios. The smal

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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.

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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

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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.

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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 S

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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.

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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 menunjukka

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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.

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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 an

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Purnomo, Niko, Windu Gata, Muhammad Romadhona Kusuma, Riadi Marta Dinata, and Modesta Binti Husna. "IMPLEMENTASI YOU ONLY LOOK ONCE v8 DALAM DETEKSI MAKANAN WARUNG TEGAL UNTUK SISTEM PERHITUNGAN HARGA OTOMATIS." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 2 (2025): 3476–83. https://doi.org/10.36040/jati.v9i2.13465.

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Warung Tegal (Warteg) merupakan usaha kuliner yang populer di Indonesia, tetapi sistem perhitungan harga makanannya masih manual, yang dapat menyebabkan kesalahan transaksi. Penelitian ini bertujuan mengembangkan sistem deteksi makanan otomatis menggunakan YOLO v8 untuk mengotomatisasi perhitungan harga.Dataset terdiri dari berbagai lauk warteg yang diproses dengan teknik augmentasi seperti pemotongan, rotasi, dan pencahayaan guna meningkatkan kinerja model. Hasil penelitian menunjukkan bahwa dalam pengujian terbaik dengan dataset 70:30 (20 epochs, batch size 16, learning rate 0.001), model YO

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Michael, Goodnews, Essa Q. Shahra, Shadi Basurra, Wenyan Wu, and Waheb A. Jabbar. "Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8." Sensors 24, no. 21 (2024): 6982. http://dx.doi.org/10.3390/s24216982.

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Detecting faulty pipelines in water management systems is crucial for ensuring a reliable supply of clean water. Traditional inspection methods are often time-consuming, costly, and prone to errors. This study introduces an AI-based model utilizing images to detect pipeline defects, focusing on leaks, cracks, and corrosion. The YOLOv8 model is employed for object detection due to its exceptional performance in detecting objects, segmentation, pose estimation, tracking, and classification. By training on a large dataset of labeled images, the model effectively learns to identify visual patterns

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Liu, Hui, Yushuo Hou, Jicheng Zhang, Ping Zheng, and Shouyin Hou. "Research on Weed Reverse Detection Methods Based on Improved You Only Look Once (YOLO) v8: Preliminary Results." Agronomy 14, no. 8 (2024): 1667. http://dx.doi.org/10.3390/agronomy14081667.

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The rapid and accurate detection of weeds is the prerequisite and foundation for precision weeding, automation, and intelligent field operations. Due to the wide variety of weeds in the field and their significant morphological differences, most existing detection methods can only recognize major crops and weeds, with a pressing need to enhance accuracy. This study introduces a novel weed detection approach that integrates the GFPN (Green Feature Pyramid Network), Slide Loss, and multi-SEAM (Spatial and Enhancement Attention Modules) to enhance accuracy and improve efficiency. This approach re

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Eko Farhan, Arief, Prajoko Prajoko, and Agung Pambudi. "PENDETEKSIAN KANDUNGAN GULA DAN KARBOHIDRAT PADA UMBI-UMBIAN DENGAN METODE YOLO (YOU ONLY LOOK ONCE) v8." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 5 (2024): 10043–50. http://dx.doi.org/10.36040/jati.v8i5.10891.

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Di Indonesia, sebagian besar asupan makanan terdiri dari karbohidrat. Setelah dikonsumsi, karbohidrat tersebut dicerna oleh enzim di tubuh manusia dan diubah menjadi glukosa. Kadar gula darah memiliki peran penting dalam mempengaruhi fluktuasi tekanan darah, sehingga mengelola tekanan darah dan kadar gula sangat penting untuk meningkatkan kualitas hidup. Asupan gula yang berlebihan, terutama akibat pola makan yang kurang sehat, dapat menyebabkan diabetes melitus, yang banyak dialami oleh masyarakat, terutama lansia. Makanan tradisional berbasis umbi-umbian menawarkan potensi sebagai sumber kar

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Rismayanti, Azizah, and Reni Rahmadewi. "DETEKSI DAN KLASIFIKASI TINGKAT KEMATANGAN BUAH MANGGA HARUM MANIS MENGGUNAKAN YOU ONLY LOOK ONCE (YOLO) V8." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 3 (2025): 3645–54. https://doi.org/10.36040/jati.v9i3.13320.

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Mangga Harum Manis adalah varietas mangga dari Probolinggo, Jawa Timur, dengan ciri bentuk jorong, sedikit berparuh, dan ujung meruncing. Penyortiran tingkat kematangan mangga Harum Manis di Probolinggo, Jawa Timur, masih dilakukan secara manual dengan metode pengamatan visual, yang dapat menyebabkan inkonsistensi dalam penentuan kualitas. Ketidakseragaman ini berpotensi memengaruhi standar distribusi dan nilai jual buah. Oleh karena itu, penelitian ini mengembangkan sistem otomatis berbasis YOLOv8 untuk mendeteksi dan mengklasifikasikan tingkat kematangan mangga harum manis menjadi tiga kateg

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Adi Permana, Arya, Muhammad Rafi Muttaqin, and Muhamad Agus Sunandar. "SISTEM DETEKSI API SECARA REAL TIME MENGGUNAKAN ALGORITMA YOU ONLY LOOK ONCE (YOLO) VERSI 8." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 5 (2024): 10395–400. http://dx.doi.org/10.36040/jati.v8i5.10847.

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Perkembangan teknologi yang pesat, terutama di bidang Artificial Intelligence (AI), telah membawa kemajuan dalam deteksi kebakaran. Data dari Badan Penanggulangan Bencana Provinsi Jawa Barat menunjukkan bahwa tahun 2019 hingga 2021 terdapat 607 kejadian kebakaran bangunan, dengan Kota Bandung mencatatkan 116 kejadian. Tingginya risiko kebakaran ini menunjukkan perlunya langkah preventif yang lebih efektif untuk melindungi masyarakat dan properti. Untuk mengatasi permasalahan tersebut, penelitian ini menggunakan algoritma You Only Look Once (YOLO)v8 untuk mendeteksi api di dalam ruangan. Metodo

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Li, Enze, Qibiao Wang, Jinzhao Zhang, Weihan Zhang, Hanlin Mo, and Yadong Wu. "Fish Detection under Occlusion Using Modified You Only Look Once v8 Integrating Real-Time Detection Transformer Features." Applied Sciences 13, no. 23 (2023): 12645. http://dx.doi.org/10.3390/app132312645.

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Fish object detection has attracted significant attention because of the considerable role that fish play in human society and ecosystems and the necessity to gather more comprehensive fish data through underwater videos or images. However, fish detection has always faced difficulties with the occlusion problem because of dense populations and underwater plants that obscure them, and no perfect solution has been found until now. To address the occlusion issue in fish detection, the following effort was made: creating a dataset of occluded fishes, integrating the innovative modules in Real-time

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Shakila, Rahman, Muhammad Hasnat Jamee Syed, Khan Rafi Jakaria, Sultana Juthi Jafrin, Abdul Aziz Sajib, and Uddin Jia. "Real-time smoke and fire detection using you only look once v8 based advanced computer vision and deep learning." International Journal of Advances in Applied Sciences (IJAAS) 13, no. 4 (2024): 987–99. https://doi.org/10.11591/ijaas.v13.i4.pp987-999.

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Fire and smoke pose severe threats, causing damage to property and the environment and endangering lives. Traditional fire detection methods struggle with accuracy and speed, hindering real-time detection. Thus, this study introduces an improved fire and smoke detection approach utilizing the you only look once (YOLO)v8-based deep learning model. This work aims to enhance accuracy and speed, which are crucial for early fire detection. The methodology involves preprocessing a large dataset containing 5,700 images depicting fire and smoke scenarios. YOLOv8 has been trained and validated, outperf

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Bajpai, Manas. "YOLO Models for Security and Surveillance Applications." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 2513–18. http://dx.doi.org/10.22214/ijraset.2024.63521.

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Abstract: Since 2015, the YOLO (You Only Look Once) series has evolved to YOLO-v8, prioritizing real-time processing and high accuracy for security and surveillance applications. Architectural enhancements in each iteration, culminating in YOLOv9, cater to rapid detection, precision, and adaptability to resource-constrained edge devices. This study examines YOLO’s evolution, emphasizing its relevance to security and surveillance contexts. Notable improvements in architecture, coupled with practical deployments for defect detection, underscore YOLO’s alignment with stringent security and survei

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Meena, Er M., and Dr G. Ramesh. "SMART BABY MONITORING SYSTEM USING YOLO V8 ALGORITHM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–16. http://dx.doi.org/10.55041/ijsrem36698.

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The Smart Baby Monitoring System using the YOLO V8 algorithm is designed to enhance infant monitoring by leveraging advanced computer vision techniques. This project utilizes YOLO (You Only Look Once) version 8, a state-of-the-art object detection algorithm, implemented with Python and frameworks like Tensor Flow or PyTorch, to detect and track objects in real-time video feeds. The system incorporates features for facial recognition to identify known caregivers and alert mechanisms for unusual activities or emergencies. The user interface provides real-time alerts, visualizations, and historic

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Rahman, Shakila, Syed muhammad Hasnat Jamee, Jakaria Khan Rafi, Jafrin Sultana Juthi, Abdul Aziz Sajib, and Jia Uddin. "Real-time smoke and fire detection using you only look once v8-based advanced computer vision and deep learning." International Journal of Advances in Applied Sciences 13, no. 4 (2024): 987. http://dx.doi.org/10.11591/ijaas.v13.i4.pp987-999.

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Fire and smoke pose severe threats, causing damage to property and the environment and endangering lives. Traditional fire detection methods struggle with accuracy and speed, hindering real-time detection. Thus, this study introduces an improved fire and smoke detection approach utilizing the you only look once (YOLO)v8-based deep learning model. This work aims to enhance accuracy and speed, which are crucial for early fire detection. The methodology involves preprocessing a large dataset containing 5,700 images depicting fire and smoke scenarios. YOLOv8 has been trained and validated, outperf

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Putra, Ramadhan Hardani, Eha Renwi Astuti, I. Komang Evan Wijaksana, Arna Fariza, Ratri Maya Sitalaksmi, and Nobuhiro Yoda. "Automated Periodontal Bone Loss Detection on Panoramic Radiographs Using You Only Look Once v8 (YOLOv8): A Retrospective AI Approach." Journal of International Oral Health 17, no. 3 (2025): 203–11. https://doi.org/10.4103/jioh.jioh_20_25.

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Abstract Aim: We aimed to evaluate the diagnostic performance of the You Only Look Once version 8 (YOLOv8) deep learning model in the automated detection of periodontal bone loss (PBL) on panoramic radiographs and assess its potential as a clinical diagnostic aid. Materials and Methods: A total of 500 annotated panoramic radiographs with PBL were retrospectively collected and randomized into training (n = 400), validation (n = 50), and testing (n = 50) datasets. Image annotation was performed using Roboflow and validated by a radiologist and periodontist. YOLOv8 models of five variants (n, s,

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Elesawy, Abdelrahman, Eslam Mohammed Abdelkader, and Hesham Osman. "A Detailed Comparative Analysis of You Only Look Once-Based Architectures for the Detection of Personal Protective Equipment on Construction Sites." Eng 5, no. 1 (2024): 347–66. http://dx.doi.org/10.3390/eng5010019.

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For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in co

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Affandi, Rikemaulani, and Budi Hartono. "Quadcopter v8: Kaji Pengolahan Citra untuk Misi Terbang Pendeteksian Keberadaan Manusia." Prosiding Industrial Research Workshop and National Seminar 14, no. 1 (2023): 567–72. http://dx.doi.org/10.35313/irwns.v14i1.5448.

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Quadcopter memiliki bentuk yang kecil sehingga leluasa untuk bergerak di tempat yang sulit dan quadcopter dapat terbang secara vertikal, yang berarti tidak perlu landasan pacu untuk terbang. Kemajuan teknologi terus berkembang dengan cepat. Teknologi dalam mendeteksi suatu objek pada saat ini sangat populer mulai dari kebutuhan dalam mendeteksi objek seperti warna, wajah, sidik jari, dan sejenisnya menjadi awal pada pengembangan aplikasi citra digital yang lebih modern. Pada penelitian ini, telah dibuat quadcopter dengan sistem pengolahan citra menggunakan metode algoritma YOLO (You Only Look

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Jung, Do-Yoon, Yeon-Jae Oh, and Nam-Ho Kim. "A Study on GAN-Based Car Body Part Defect Detection Process and Comparative Analysis of YOLO v7 and YOLO v8 Object Detection Performance." Electronics 13, no. 13 (2024): 2598. http://dx.doi.org/10.3390/electronics13132598.

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The main purpose of this study is to generate defect images of body parts using a GAN (generative adversarial network) and compare and analyze the performance of the YOLO (You Only Look Once) v7 and v8 object detection models. The goal is to accurately judge good and defective products. Quality control is very important in the automobile industry, and defects in body parts directly affect vehicle safety, so the development of highly accurate defect detection technology is essential. This study ensures data diversity by generating defect images of car body parts using a GAN and through this, co

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Serttaş, Esma, and Fatih Gül. "YOLO V8 Algoritması ile Otomatik Plaka Tanıma ve Görselleştirme Sistemi." Bilişim Teknolojileri Dergisi 18, no. 1 (2025): 1–10. https://doi.org/10.17671/gazibtd.1506041.

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Bu çalışma ile, belirli bir mesafeye yerleştirilen bir kamera ile YOLO (You Only Look Once) V8 algoritmasını kullanarak aracın üzerindeki plakayı otomatik olarak tanıyan ve görselleştiren bir sistem tasarlanmıştır. YOLO V8, gelişmiş bilgisayarlı görü yeteneklerine sahip olmakla birlikte doğrudan plaka tanıma modeli içermemektedir. Bu çalışma ile güvenlik önlemleri gerektiren alanlarda insan gücünü ve maliyeti en aza indirerek verimli şekilde kullanılabilir bir model önerilmiştir. Plaka veri seti, bilgisayarlı görü modeli ortamı Roboflow kullanılarak oluşturulmuş ve yapay sinir ağı eğitim model

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Hussain, Muhammad. "YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection." Machines 11, no. 7 (2023): 677. http://dx.doi.org/10.3390/machines11070677.

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Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but efficient computational parameters. This principle has been found within the DNA of all YOLO variants with increasing intensity, as the variants evolve addressing the requirements of automated quality inspection within the industrial surface defect detection domain, such as the need for fast detection, high accuracy, and d

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Noer Fadilah, Rina Putri, Rasmi Rikmasari, Saiful Akbar, and Arlette Suzy Setiawan. "IDCCD: evaluation of deep learning for early detection caries based on ICDAS." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 381. https://doi.org/10.11591/ijeecs.v38.i1.pp381-392.

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Dental caries is a common oral disease in children, influenced by environmental, psychological, behavioral, and biological factors. The American academy of pediatric dentistry recommends screening from the time the first tooth erupts or at one year of age to prevent caries, which mostly affects children from racial and ethnic minorities. In Indonesia, the 2023 health survey reported a caries prevalence of 84.8% in children aged 5-9 years. This research introduces early caries detection using three deep learning models: faster-RCNN, you only look once (YOLO) V8, and detection transformer (DETR)

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Rina, Putri Noer Fadilah Rasmi Rikmasari Saiful Akbar Arlette Suzy Setiawan. "IDCCD: evaluation of deep learning for early detection caries based on ICDAS." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 1 (2025): 381–92. https://doi.org/10.11591/ijeecs.v38.i1.pp381-392.

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Dental caries is a common oral disease in children, influenced by environmental, psychological, behavioral, and biological factors. The American academy of pediatric dentistry recommends screening from the time the first tooth erupts or at one year of age to prevent caries, which mostly affects children from racial and ethnic minorities. In Indonesia, the 2023 health survey reported a caries prevalence of 84.8% in children aged 5-9 years. This research introduces early caries detection using three deep learning models: faster-RCNN, you only look once (YOLO) V8, and detection transformer (DETR)

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Cong, Peichao, Jiaxing Li, Junjie Liu, Yixuan Xiao, and Xin Zhang. "SEG-SLAM: Dynamic Indoor RGB-D Visual SLAM Integrating Geometric and YOLOv5-Based Semantic Information." Sensors 24, no. 7 (2024): 2102. http://dx.doi.org/10.3390/s24072102.

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Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of visual SLAM systems, this study proposes a dynamic visual SLAM (SEG-SLAM) system based on the orientated FAST and rotated BRIEF (ORB)-SLAM3 framework and you only look once (YOLO)v5 deep-learning method. First, based on the ORB-SLAM3 framework, the YOLOv5 deep-learning method is used to construct a fusion module fo

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TS, Prof Nishchitha. "Real Time Object Detection in Autonomous Vehicle Using Yolo V8." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48914.

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Abstract Autonomous vehicles rely heavily on real-time object detection to ensure safe and efficient navigation in dynamic environments. This paper explores the implementation of YOLOv8 (You Only Look Once, version 8), a state-of-the-art deep learning model for object detection, within autonomous driving systems. YOLOv8 offers enhanced speed, accuracy, and lightweight deployment capabilities compared to its predecessors, making it highly suitable for real-time applications. The model is trained and evaluated on datasets such as KITTI and COCO to detect and classify various objects including pe

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Malik, Nirupama, and Amiyajyoti Nayak. "Artificial Intelligence System Based Personal Protective Equipment Detection for Construction Site Safety using YOLOv8." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2675–83. https://doi.org/10.22214/ijraset.2025.67829.

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Abstract: This paper addresses the limitations of current deep learning models for detecting Personal Protective Equipment (PPE) on construction sites, where performance enhancement is crucial. This paper use You Only Look Once (YOLO) architecture, focusing on ten categories: 'Hardhat', 'Mask', 'NO-Hardhat', 'NO-Mask', 'NO-Safety Vest', 'Person', 'Safety Cone', 'Safety Vest', 'machinery', 'vehicle'. A new high-quality dataset, named PPE dataset from Roboflow, was created, comprising 1,330 images that reflect real construction environments, various poses, angles, distances, and multiple PPE typ

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Hutabarat, Rizky Theofilus, and Robert Kurniawan. "Deteksi Sampah di Permukaan Sungai menggunakan Convolutional Neural Network dengan Algoritma YOLOv8." Seminar Nasional Official Statistics 2024, no. 1 (2024): 537–48. http://dx.doi.org/10.34123/semnasoffstat.v2024i1.2099.

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Meningkatnya jumlah sampah padat di sungai menjadi salah satu masalah utama di daerah perkotaan, karena sungai yang dipenuhi sampah bisa berujung pada masalah seperti banjir atau berbagai penyakit. Tujuan dari penelitian ini adalah untuk membangun model object detection menggunakan Convolutional Neural Network (CNN) dengan algoritma YOLOv8 (You Only Look Once v8), dan mengimplementasikan model tersebut untuk mendeteksi sampah mengapung di permukaan Sungai Ciliwung. Model yang digunakan adalah YOLOv8, karena terkenal dengan kecepatan dan akurasi yang tinggi. Data yang digunakan untuk membangun

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Baek, Jong-Won, Jung-Il Kim, and Chang-Bae Kim. "Deep learning-based image classification of sea turtles using object detection and instance segmentation models." PLOS ONE 19, no. 11 (2024): e0313323. http://dx.doi.org/10.1371/journal.pone.0313323.

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Sea turtles exhibit high migratory rates and occupy a broad range of habitats, which in turn makes monitoring these taxa challenging. Applying deep learning (DL) models to vast image datasets collected from citizen science programs can offer promising solutions to overcome the challenge of monitoring the wide habitats of wildlife, particularly sea turtles. Among DL models, object detection models, such as the You Only Look Once (YOLO) series, have been extensively employed for wildlife classification. Despite their successful application in this domain, detecting objects in images with complex

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Jiang, Long, Weitao Chen, Hongtai Shi, Hongwen Zhang, and Lei Wang. "Cotton-YOLO-Seg: An Enhanced YOLOV8 Model for Impurity Rate Detection in Machine-Picked Seed Cotton." Agriculture 14, no. 9 (2024): 1499. http://dx.doi.org/10.3390/agriculture14091499.

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The detection of the impurity rate in machine-picked seed cotton is crucial for precision agriculture. This study proposes a novel Cotton-YOLO-Seg cotton-impurity instance segmentation algorithm based on the you only look once version 8 small segmentation model (Yolov8s-Seg). The algorithm achieves precise pixel-level segmentation of cotton and impurities in seed cotton images and establishes a detection model for the impurity rate, enabling accurate detection of the impurity rate in machine-picked cotton. The proposed algorithm removes the Pyramid 4 (P4) feature layer and incorporates Multi-S

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Nusman, Bayu, Aviv Yuniar Rahman, and Rangga Pahlevi Putera. "LOBSTER AGE DETECTION USING DIGITAL VIDEO-BASED YOLO V8 ALGORITHM." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 1155–63. https://doi.org/10.52436/1.jutif.2024.5.4.2144.

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Lobster is an aquatic animal that has high economic value in the fishing industry. Demand for lobster in both domestic and export markets continues to increase thanks to its delicious meat and a variety of desirable dishes. Indonesia, especially Java Island, contributes significantly to the national lobster production. However, the current manual determination of lobster age has limitations such as complexity, time required, and subjectivity in assessment.To overcome this problem, this research proposes the detection of lobster age using the YOLO (You Only Look Once) method, specifically the Y

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Pawłowski, Jakub, Marcin Kołodziej, and Andrzej Majkowski. "Implementing YOLO Convolutional Neural Network for Seed Size Detection." Applied Sciences 14, no. 14 (2024): 6294. http://dx.doi.org/10.3390/app14146294.

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The article presents research on the application of image processing techniques and convolutional neural networks (CNN) for the detection and measurement of seed sizes, specifically focusing on coffee and white bean seeds. The primary objective of the study is to evaluate the potential of using CNNs to develop tools that automate seed recognition and measurement in images. A database was created, containing photographs of coffee and white bean seeds with precise annotations of their location and type. Image processing techniques and You Only Look Once v8 (YOLO) models were employed to analyze

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Sk, Sadik. "Effective Traffic Signal Control System Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31815.

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Traffic congestion is becoming a serious problem with a large number of vehicles on the roads. The present traffic system is a timer-based system that operates irrespective of the amount of traffic if there exists an ambulance. So, this Deep Learning project is designed in such a way that the traffic control system is based on vehicle density in a lane and also detecting the ambulance’s lane and let that particular lane pass considering as a first priority. In fact, we use computer vision to have the characteristics of the competing traffic rows at the signals. This is done by a object detecti

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Zhang, Jianing. "Evolution of YOLO: A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10." Applied and Computational Engineering 146, no. 1 (2025): 15–23. https://doi.org/10.54254/2755-2721/2025.21591.

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This paper presents a systematic comparative analysis of three versions of the YOLO (You Only Look Once) target detection algorithm - YOLOv5, YOLOv8 and YOLOv10. Through experiments on the VOC2012 dataset (converted to COCO format), this paper evaluates the versions in terms of multiple dimensions such as detection performance, inference speed and model complexity. The experimental results show that the detection accuracy and robustness significantly improve with version iteration and the mAP of v8 v10 is improved by 6.69% and 9.12% relative to v5, However, the number of model parameters incre

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Şimşek, Mehmet Ali, and Ahmet Sertbaş. "AUTOMATIC DETECTION OF MENISCUS TEARS FROM KNEE MRI IMAGES USING DEEP LEARNING: YOLO V8, V9, AND V10 SERIES." Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 28, no. 1 (2025): 292–308. https://doi.org/10.17780/ksujes.1559862.

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Meniscal tears are a disease that occurs in the knee joint and negatively affects people's mobility. In this study, the performance of the state-of-the-art (SOTA) YOLO (You Only Look Once) models, in particular YOLOv8l, YOLOv8x, YOLOv9c, YOLOv9e, YOLOv10l, and YOLOv10x, for the detection of meniscal tears was investigated. The algorithms were trained and tested with data from magnetic resonance imaging (MRI). In our study, the YOLOv9e model showed the highest performance and achieved the best results in the training phase with a mAP50 of 0.91807, a precision of 0.87684, a recall of 0.93871 and

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Zhao, Ziyu, Zhedong Ge, Mengying Jia, Xiaoxia Yang, Ruicheng Ding, and Yucheng Zhou. "A Particleboard Surface Defect Detection Method Research Based on the Deep Learning Algorithm." Sensors 22, no. 20 (2022): 7733. http://dx.doi.org/10.3390/s22207733.

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Particleboard surface defects have a significant impact on product quality. A surface defect detection method is essential to enhancing the quality of particleboard because the conventional defect detection method has low accuracy and efficiency. This paper proposes a YOLO v5-Seg-Lab-4 (You Only Look Once v5 Segmentation-Lab-4) model based on deep learning. The model integrates object detection and semantic segmentation, which ensures real-time performance and improves the detection accuracy of the model. Firstly, YOLO v5s is used as the object detection network, and it is added into the SELay

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Motru, Vijaya Raju, Subbarao P. Krishna, and Babu A. Sudhir. "Early disease detection of black gram plant leaf using cloud computing based YOLO V8 model." i-manager's Journal on Information Technology 12, no. 4 (2023): 18. http://dx.doi.org/10.26634/jit.12.4.20209.

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Plant diseases pose a major threat to agricultural productivity and economies dependent on it. Monitoring plant growth and phenotypes is vital for early disease detection. In Indian agriculture, black-gram (Vigna mungo) is an important pulse crop afflicted by viral infections like Urdbean Leaf Crinkle Virus (ULCV), causing stunted growth and crinkled leaves. Such viral epidemics lead to massive crop losses and financial distress for farmers. According to the FAO, plant diseases cost countries $220 billion annually. Hence, there is a need for quick and accurate diagnosis of crop diseases like U

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Zhong, Zixuan. "Pedestrian detection and gender recognition utilizing YOLO and CNN algorithms." Applied and Computational Engineering 31, no. 1 (2024): 133–38. http://dx.doi.org/10.54254/2755-2721/31/20230136.

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As crowd-based activities continue to surge in locales such as markets and restaurants, the significance of understanding pedestrian flow is increasingly evident. Over recent years, advancements in dynamic pedestrian detection, facilitated by the YOLO (You Only Look Once) algorithm, have seen widespread application in areas like crowd management and occupancy estimation. The YOLO algorithm has demonstrated high accuracy and efficiency in real-time object tracking and counting. However, for specific use cases, data derived solely from monitoring pedestrian flows may prove inadequate. This study

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Khubrani, Mousa Mohammed, Fathe Jeribi, Ali Tahir, and Abdulnasser Abdulwakil Metwally. "Panoramic Dental X-Ray Restorative Elements Segmentation using Hybrid Deep Learning." WSEAS TRANSACTIONS ON COMPUTERS 23 (December 31, 2024): 328–35. https://doi.org/10.37394/23205.2024.23.32.

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Panoramic radiography is a commonly used imaging technique for dental X-rays, it is used as a diagnostics tool in dentistry. The study introduced a hybrid deep learning approach for detecting and segmenting dental restorative elements from panoramic dental X-rays. By integrating the You Look Only Once (YOLO v8) model for object detection and the Segment Anything Model (SAM) for segmentation, the aim is to enhance the identification of different dental restorative elements such as dental implants, crowns, fillings, and root canals. The datasets of the study comprised 1290 dental X-ray images. T

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Cheng, Yuxuan, Yidan Huang, Jingjing Zhang, Xuehong Zhang, Qiaohua Wang, and Wei Fan. "Robust Detection of Cracked Eggs Using a Multi-Domain Training Method for Practical Egg Production." Foods 13, no. 15 (2024): 2313. http://dx.doi.org/10.3390/foods13152313.

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The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in pract

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Venkateswarlu, K. "YOLO Based Advanced Smart Traffic Assistance Platform for Number Plate and Helmet Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 4204–8. http://dx.doi.org/10.22214/ijraset.2023.54414.

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Abstract: Now a days road accidents are one of the major causes that are leading to human death. However the most common reason for motorcycle deaths is because many fail to confirm to the law of wearing helmet. Here is the software using YOLO V8 to recognize the motorbike drivers , who are not obeying helmet law in an automated way. The helmet and license plate detection system using YOLO V8 is a computer vision technology-based system that utilizesthe You Only Look Once (YOLO) objectdetection algorithm to detect helmets and license plates in real-time. The system is designed to improve safet

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Xia, Chen. "Rapid Strawberry Ripeness Detection And 3D Localization of Picking Point Based on Improved YOLO V8-Pose with RGB-Camera." Journal of Electrical Systems 20, no. 3s (2024): 2171–81. http://dx.doi.org/10.52783/jes.1840.

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Accurate identification of strawberries at different growth stages as well as determination of optimal picking points by strawberry picking robots is a key issue in the field of agricultural automation. In this paper, a fast detection method of strawberry ripeness and picking point based on improved YOLO V8-Pose (You Only Look Once) and RGB-D depth camera is proposed to address this problem. By comparing the YOLO v5-Pose, YOLO v7-Pose, and YOLO v8-Pose models, it is determined to use the YOLO v8-Pose model as the fundamental model for strawberry ripeness and picking point detection. For the sa

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