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Journal articles on the topic 'Yolo (you only look once)'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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Anorbaev, Akmal, Prerna Agarwal, and Pranav Shrivastava. "You Only Look Once (YOLO): Object Detection Algorithm." International Journal of Computer Applications ICAIDSC2023, no. 3 (2025): 9–14. https://doi.org/10.5120/icaidsc202419.

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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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Andi, Ilham, Mutmainnah Muchtar, and Jayanti Yusmah Sari. "Mask Detection Using the YOLO (You Only Look Once) Method." Jurnal Media Informasi Teknologi 1, no. 1 (2024): 1–12. http://dx.doi.org/10.69616/mit.v1i1.165.

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The COVID-19 pandemic has emphasized the importance of wearing masks as a preventive measure. To facilitate mask detection and ensure compliance, computer vision techniques have been widely utilized. This research aims to develop a mask detection system using the YOLO (You Only Look Once) method. YOLO is a real-time object detection method that provides accurate and efficient results. The proposed system utilizes a pre-trained YOLO model trained on a dataset comprising images of individuals with and without masks. The YOLO model can detect and locate faces, as well as differentiate between ind

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Ovide, Decroly Wisnu Ardhi, Retnaningsih Soeprobowati Tri, Adi Kusworo, Prakasa Esa, and Rachman Arief. "Enhanced you only look once approach for automatic phytoplankton identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3426–36. https://doi.org/10.11591/ijai.v13.i3.pp3426-3436.

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Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YO

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Wisnu Ardhi, Ovide Decroly, Tri Retnaningsih Soeprobowati, Kusworo Adi, Esa Prakasa, and Arief Rachman. "Enhanced you only look once approach for automatic phytoplankton identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3426. http://dx.doi.org/10.11591/ijai.v13.i3.pp3426-3436.

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<p>Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of

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NAGAGOPIRAJU, Dr V., SUVARNA PINNINTI, ANJAMMA TAMMA, SAI TEJA KAJJAYAM, and KALESHAVALI KAKARLA. "ADVANCED WILD ANIMAL DETECTION AND ALERT SYSTEM USING THE YOLO V5 MODEL POWERED BY AI." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 1 (2024): 142–45. http://dx.doi.org/10.61841/turcomat.v15i1.14556.

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An advanced wild animal detection and alert system using you only look once version5 (YOLO V5) model. The system utilizes you only look once version5 (YOLO V5) object detection algorithm to identify wild animals and alert users to their presence in real-time. The system employs a camera to capture real-time video, which is then sent to a computer running you only look once version5 (YOLO V5) algorithm. When the system detects a wild animal, it sends an alert to the wild animal by playing any sounds like bullets firing. The system is expected to have a significant impact on the safety of people

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Wijanarko, Restu Gilang, Afu Ichsan Pradana, and Dwi Hartanti. "IMPLEMENTASI DETEKSI DRONE MENGGUNAKAN YOLO (You Only Look Once)." JURNAL FASILKOM 14, no. 2 (2024): 437–42. https://doi.org/10.37859/jf.v14i2.7374.

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Penelitian ini berfokus pada pengembangan sistem deteksi drone yang akurat dan efektif berbasis teknologi kamera. Penggunaan drone yang semakin meningkat di berbagai sektor, seperti fotografi udara, pemantauan lingkungan, pemetaan topografi, pengiriman barang, inspeksi infrastruktur, dan pertanian presisi, telah membawa banyak manfaat. Namun, penggunaan drone yang tidak bertanggung jawab juga dapat menimbulkan masalah serius, seperti pelanggaran privasi, risiko keamanan, dan gangguan pada operasi penerbangan. Untuk mengatasi tantangan ini, penelitian ini mengusulkan pendekatan deteksi drone me

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Mahfudi, Isa, Ahmad Rozak Setia Nugraha, and Azam Muzakhim Imammuddin. "Implementasi You Only Look Once (YOLO) dalam Deteksi Telur Menetas pada Reptil." JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) 6, no. 2 (2024): 177–84. https://doi.org/10.26905/jasiek.v6i2.13525.

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Saat ini reptil dijadikan hewan peliharaan karena perawatan yang mudah dan warna motif yang beragam salah satunya adalah Leopard Gecko. Perawatan Leopard Gecko yang baru menetas berbeda dari yang dewasa karena Leopard Gecko yang baru menetas harus segera ditempatkan di kandang yang diberi alas tisu dan disemprot air untuk menghindari kehilangan air. penelitian ini bertujuan untuk mengembangkan sistem deteksi telur menetas menggunakan YOLO (You Only Look Once). Hasil penelitian menunjukkan bahwa algoritma YOLO dapat digunakan untuk mendeteksi telur Leopard Gecko menetas secara real-time. persen

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Ghozali, Mohammad Shiddiq. "PEMBUATAN PENDETEKSI OBYEK DENGAN METODE YOU ONLY LOOK ONCE (YOLO) UNTUK AUTOMATED TELLER MACHINE (ATM)." Majalah Ilmiah UNIKOM 17, no. 1 (2019): 69–76. http://dx.doi.org/10.34010/miu.v17i1.2225.

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Perkembangan Teknologi Informasi dan Komunikasi begitu pesat di zaman sekarang ini. Diikuti pula dengan perkembangan di bidang Artificial Intelligence (AI) atau Kecerdasan Buatan. Di Indonesia sendiri masih belum begitu populer dikalangan masyarakat akan tetapi perusahaan-perusahaan IT berlomba-lomba menciptakan inovasi dibidang Kecerdasan Buatan dan penerapan Kecerdasan Buatan disegala aspek kehidupan. Contoh kasus di Automated Teller Machine (ATM), seringkali terjadi kejahatan di ATM seperti pengintaian nomor pin, skimming, lebanese loop dan kejahatan lainnya. Walaupun di ATM sudah terdapat

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Wasril, Abi Rachman, Mohammad Shiddiq Ghozali, and M. Banu Mustafa. "PEMBUATAN PENDETEKSI OBYEK DENGAN METODE YOU ONLY LOOK ONCE (YOLO) UNTUK AUTOMATED TELLER MACHINE (ATM)." Majalah Ilmiah UNIKOM 17, no. 1 (2019): 69–76. http://dx.doi.org/10.34010/miu.v17i1.2240.

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Perkembangan Teknologi Informasi dan Komunikasi begitu pesat di zaman sekarang ini. Diikuti pula dengan perkembangan di bidang Artificial Intelligence (AI) atau Kecerdasan Buatan. Di Indonesia sendiri masih belum begitu populer dikalangan masyarakat akan tetapi perusahaan-perusahaan IT berlomba-lomba menciptakan inovasi dibidang Kecerdasan Buatan dan penerapan Kecerdasan Buatan disegala aspek kehidupan. Contoh kasus di Automated Teller Machine (ATM), seringkali terjadi kejahatan di ATM seperti pengintaian nomor pin, skimming, lebanese loop dan kejahatan lainnya. Walaupun di ATM sudah terdapat

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Maulidiansyah, Maulidiansyah, and Moh Ainol Yaqin. "Deteksi Tumpukan Sampah dengan Metode You Only Look Once (YOLO)." TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora 4, no. 2 (2023): 76–79. http://dx.doi.org/10.33650/trilogi.v4i2.6185.

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Sampah berasal dari rumah, pertanian, perkantoran, perusahaan, rumah sakit, pasar, dll. Sampah merupakan objek jelas yang bisa dilihat dengan mata telanjang akan tetapi masyarakat pura pura buta dengan sampah yang ada di hadapan matanya dikarenakan kurangnya kesadaran diri. Kurangnya kesadaran diri di masyarakat dapat menimbulkan risiko bencana alam dan penyakit. Oleh karena itu, Anda perlu mengawasi masyarakat dalam hal pembuangan sampah. Pemantauan oleh pemerintah dan dinas kebersihan diperlukan agar masyarakat sadar akan resiko sampah. Dengan adanya uraian tersebut, maka penelitian ini dila

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Li, Guangbo, Rui Jian, Xie Jun, and Guolong Shi. "A Review of You Only Look Once Algorithms in Animal Phenotyping Applications." Animals 15, no. 8 (2025): 1126. https://doi.org/10.3390/ani15081126.

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Animal phenotyping recognition is a pivotal component of precision livestock management, holding significant importance for intelligent farming practices and animal welfare assurance. In recent years, with the rapid advancement of deep learning technologies, the YOLO algorithm—as the pioneering single-stage detection framework—has revolutionized the field of object detection through its efficient and rapid approach and has been widely applied across various agricultural domains. This review focuses on animal phenotyping as the research target structured around four key aspects: (1) the evoluti

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Hassan, Salam Abdul-Ameer, Jaleel Hassan Hassan, and Hameedi Abdullah Salma. "Development smart eyeglasses for visually impaired people based on you only look once." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 20, no. 1 (2022): 109–17. https://doi.org/10.12928/telkomnika.v20i1.22457.

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Visually impaired people are facing many problems in their life. One of these problems is how they can find the objects in their indoor environment. This research was presented to assists visually impaired people in finding the objects in office. Object detection is a method used to detect the objects in images and videos. Many algorithms used for object detection such as convolutional neural network (CNN) and you only look once (YOLO). The proposed method was YOLO which outperforms the other algorithms such as CNN. In CNN the algorithm splits the image into regions. These regions sequentially

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Rizki, Yoze, Yogi Alfinaldo, Soni, Yandiko Saputra Sy, and Rahmad Firdaus. "Klasifikasi Kebakaran Hutan Dan Lahan Dengan Algoritma You Only Learn One Representation." Jurnal CoSciTech (Computer Science and Information Technology) 4, no. 3 (2023): 832–37. http://dx.doi.org/10.37859/coscitech.v4i3.6434.

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Kawasan hutan memiliki fungsi sebagai penampung karbon dioksida serta penghasil oksigen yang berasal dari pepohonan dan tumbuh-tumbuhan. Fungsi hutan sangat penting bagi kehidupan maka hutan sangat dilindungi. Salah satu solusi yang dapat dilakukan adalah dengan melakukan tindakan pencegahan yaitu pemantauan titik api pada kawasan hutan dan lahan melalui udara. Penelitian ini dilakukan pengujian dengan menggunakan dataset yang sama dengan algoritma YOLO (You Only Look Once) terhadap algoritma You Only Learn One Representation (YOLOR) dengan model pembagian data train sebanyak 1188 data gambar

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Haval, Abhijeet Madhukar, and Md Afzal. "Aquatic object detection using YOLO (you only look once) algorithm." International Journal of Aquatic Research and Environmental Studies 4, S1 (2024): 52–57. https://doi.org/10.70102/ijares/v4s1/9.

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The programmed grouping of marine species in view of pictures is a difficult errand for which different arrangements have been progressively given in the beyond twenty years. Seas are complicated environments, hard to get to, and frequently the pictures got are of inferior quality. In such cases, creature arrangement becomes monotonous. Subsequently, it is much of the time important to apply improvement or pre-handling procedures to the pictures, prior to applying grouping calculations. The goal is to develop a deep learning system that is both extremely accurate and efficient, utilizing the Y

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Li, Liyun. "A special target detection called You Only Looks Once." Applied and Computational Engineering 39, no. 1 (2024): 92–102. http://dx.doi.org/10.54254/2755-2721/39/20230585.

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This paper proposes a unique target detection called You Only Look Once (YOLO for short) and a recognition method. Unlike the previous idea of many classifiers with object detection functions, the object detection box is set as a spatially separated bounding box and the regression problem of related class probabilities is realized. The neural network model can directly scan the entire image during testing, predicting bounding boxes and class probabilities from the complete picture. At the same time, because the whole detection channel relies on a single neural network, it is more straightforwa

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Irfan, Irfan, Andi Patombongi, and Cakra Cakra. "PELATIHAN DAN PENGUJIAN YOLO (YOU ONLY LOOK ONCE) UNTUK MENDETEKSI PLAT KENDARAAN." Simtek : jurnal sistem informasi dan teknik komputer 8, no. 2 (2023): 404–11. http://dx.doi.org/10.51876/simtek.v8i2.362.

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Metode YOLO telah banyak dimanfaatkan oleh peneliti untuk keperluan deteksi plat kendaraan, terutama dalam konteks pengenalan plat kendaraan mobil atau motor di jalur lalu lintas. Dalam proses deteksi, peneliti menggunakan dataset yang telah disiapkan untuk melatih algoritma YOLO (You Only Look Once) guna mencapai tingkat kepercayaan (confidence) yang akurat. Hasil pengujian menunjukkan bahwa proses deteksi pada video di jalur lalu lintas mampu mengenali plat kendaraan mobil atau motor dengan tingkat kepercayaan di atas 60%, dengan jarak deteksi sekitar 5 meter. Sementara itu, deteksi mengguna

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Rahma, Lusiana, Hadi Syaputra, A. Haidar Mirza, and Susan Dian Purnamasari. "Objek Deteksi Makanan Khas Palembang Menggunakan Algoritma YOLO (You Only Look Once)." Jurnal Nasional Ilmu Komputer 2, no. 3 (2021): 213–32. http://dx.doi.org/10.47747/jurnalnik.v2i3.534.

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Deep learning is a part of machine learning method that uses artificial neural network (ANN). The type of learning in deep learning can be supervised, semi-supervised, and unsupervised [7] . CNN & RNN (Supervised) and RBM & Autoencoder (Unsupervised) are deep learning algorithms. All of the above algorithms have uses in their respective fields, depending on what we want to use them for. One of the most frequently used cases for deep learning is object detection and classification. The Convolutional Neural Network (CNN) algorithm is the most widely used algorithm for object detection ca

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Federe, Prince Jewel, Andrew Emil Pagador, and Avegail Ruiz. "CAMARINE: A FISH SPECIES RECOGNITION SYSTEM THROUGH YOU ONLY LOOK ONCE." PUP Journal of Science and Technology 14, no. 1 (2024): 1–14. https://doi.org/10.70922/f5czgv24.

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Data collection for marine sciences has always been arduous, mainly because of cost. The higher the cost is, the slower the growth of knowledge. To ease that cost, Camarine was built. An application for fish species recognition, Camarine used the algorithm You Only Look Once (YOLO) to seep through convolutional layers to detect and identify fish species. Twelve species of fish were categorized according to likeness and lack thereof. Over 4800 images were augmented to sport better results for the trained model. For testing, around 600 images were collected in various locations, including experi

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Gehad, Saleh Ahmed Mohammed, Mat Diah Norizan, Ibrahim Zaidah, and Jamil Nursuriati. "Vehicle detection and classification using three variations of you only look once algorithm." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 3 (2023): 442–52. https://doi.org/10.11591/ijres.v12.i3pp442-452.

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Vehicle detection and classification are essential for advanced driver assistance systems (ADAS) and even traffic camera surveillance. Yet, it is challenging due to complex backgrounds, varying illumination intensities, occlusions, vehicle size, and type variations. This paper aims to apply you only look once (YOLO) since it has been proven to produce high object detection and classification accuracy. There are various versions of YOLO, and their performances differ. An investigation on the detection and classification performance of YOLOv3, YOLOv4, and YOLOv5 has been conducted. The training

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Harun, Ahmad, Mustakim Mustakim, and Oktaf Brilian Kharisma. "Implementasi Deep Learning Menggunakan Metode You Only Look Once untuk Mendeteksi Rokok." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 1 (2023): 107. http://dx.doi.org/10.30865/mib.v7i1.5409.

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Cigarettes are processed products from tobacco products which are used by burning and then smoked. Smoking activities are often found in everyday life, including in public infrastructure. The approach taken to prevent this activity generally uses manual information or human intervention. In terms of this approach, there are often many problems and failures due to the lack of manpower and supporting rules. Therefore, this study was structured with the aim of being able to detect smoking objects in real time using the You Only Look Once (YOLO) method. YOLO which is based on deep learning is very

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Zhao, Meng, Wenbo Wang, Qunyan Ren, Haiyan Ni, Xu Xiao, and Li Ma. "Modified you-only-look-once model for joint source detection and azimuth estimation in a multi-interfering underwater acoustic environment." Journal of the Acoustical Society of America 153, no. 4 (2023): 2393. http://dx.doi.org/10.1121/10.0017828.

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The you-only-look-once (YOLO) model identifies objects in complex images by framing detection as a regression problem with spatially separated boundaries and class probabilities. Object detection from complex images is somewhat similar to underwater source detection from acoustic data, e.g., time-frequency distributions. Herein, YOLO is modified for joint source detection and azimuth estimation in a multi-interfering underwater acoustic environment. The modified you-only-look-once (M-YOLO) input is a frequency-beam domain (FBD) sample containing the target and multi-interfering spectra at diff

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Sarosa, M., N. Muna, and E. Rohadi. "Detection of natural disaster victims using You Only Look Once (YOLO)." IOP Conference Series: Materials Science and Engineering 1098, no. 3 (2021): 032076. http://dx.doi.org/10.1088/1757-899x/1098/3/032076.

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S, Sirajudeen, and Sudha S. "A Review - Smoke-Fire Detection and YOLO (You Only Look Once)." International Research Journal on Advanced Science Hub 5, no. 08 (2023): 248–56. http://dx.doi.org/10.47392/irjash.2023.051.

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Afifuddin Arif Shihabuddin Arip, Norazlianie Sazali, Kumaran Kadirgama, Ahmad Shahir Jamaludin, Faiz Mohd Turan, and Norhaida Ab. Razak. "Object Detection for Safety Attire Using YOLO (You Only Look Once)." Journal of Advanced Research in Applied Mechanics 113, no. 1 (2024): 37–51. http://dx.doi.org/10.37934/aram.113.1.3751.

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Personal protective equipment (PPE) usage is mandated for all employees to prevent workplace accidents and foster a safe and healthy work environment. Using YOLOv8 machine learning and Google Colab's web-based development environment, this research aims to create an immediate detection system for PPE violations in the workplace. By keeping track of PPE compliance, the system is intended to increase workplace safety and prevent accidents. The dataset is collected through a mixture of real-life image gathering and internet datasets. Various images are collected that aim to train the model to det

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Mauladany, Muhammad Ibna, Bagus Fatkhurrozi, and Rheza Ari Wibowo. "Deteksi Penyakit Daun Durian dengan Algoritma YOLO (You Only Look Once)." AVITEC 6, no. 1 (2024): 73. http://dx.doi.org/10.28989/avitec.v6i1.2067.

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Permasalahan yang dialami petani durian salah satunya adalah serangan penyakit terhadap daun sehingga mengganggu proses produksi buah. Penyakit yang sering menyerang daun durian adalah bercak daun dan hawar daun. Penelitian ini memiliki tujuan untuk menerapkan teknologi kecerdasan buatan yang dapat membantu mengenali, mengamati serta mendeteksi penyakit daun durian secara efektif. Algoritma deteksi objek menggunakan YOLO (You Only Look Once) merupakan bagian dari sistem kecerdasan buatan digunakan dalam penelitian ini. Objek yang dideteksi dalam penelitian ini dibagi menjadi 3 kelas yaitu berc

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Reddy, Dr M. V. Subba. "Implement YOLO(You Only Look Once)to Detect Objects in Image." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 1866–69. https://doi.org/10.61137/ijsret.vol.11.issue2.315.

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Adamu, Zainab Bala, Auwal Hussaini, Badamasi Imam Ya’u, Kabiru Ibrahim Musa, and Fatimah Abubakar Muhammad. "Deep Learning For Oil Spill Detection Based on You Look Only Once (YOLO) Approach: A Review." International Journal of Research Publication and Reviews 5, no. 11 (2024): 7010–30. https://doi.org/10.55248/gengpi.5.1124.3418.

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Mohammed, Gehad Saleh Ahmed, Norizan Mat Diah, Zaidah Ibrahim, and Nursuriati Jamil. "Vehicle detection and classification using three variations of you only look once algorithm." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 3 (2023): 442. http://dx.doi.org/10.11591/ijres.v12.i3.pp442-452.

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<p><span>Vehicle detection and classification are essential for advanced driver assistance systems (ADAS) and even traffic camera surveillance. Yet, it is challenging due to complex backgrounds, varying illumination intensities, occlusions, vehicle size, and type variations. This paper aims to apply you only look once (YOLO) since it has been proven to produce high object detection and classification accuracy. There are various versions of YOLO, and their performances differ. An investigation on the detection and classification performance of YOLOv3, YOLOv4, and YOLOv5 has been con

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Sree, S. Renuka. "NEW ERA OF VISION TO ENVISION USING YOLO." International Scientific Journal of Engineering and Management 03, no. 03 (2024): 1–9. http://dx.doi.org/10.55041/isjem01424.

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We provide a new approach to crowd counting in this research that makes use of the You Only Look Once (YOLO) algorithm. We show how well YOLO performs in precisely identifying and counting people in congested environments. Our method seeks to overcome the difficulties associated with crowd observation and analysis in real time. Urban planning, public safety, and event management are just a few of the many areas where crowd counting is important and dynamic. Occlusions, size variations, and congested settings are only a few of the challenges that traditional approaches frequently face in provid

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Cao, Chang-Yu, Jia-Chun Zheng, Yi-Qi Huang, Jing Liu, and Cheng-Fu Yang. "Investigation of a Promoted You Only Look Once Algorithm and Its Application in Traffic Flow Monitoring." Applied Sciences 9, no. 17 (2019): 3619. http://dx.doi.org/10.3390/app9173619.

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We propose a high-performance algorithm while using a promoted and modified form of the You Only Look Once (YOLO) model, which is based on the TensorFlow framework, to enhance the real-time monitoring of traffic-flow problems by an intelligent transportation system. Real-time detection and traffic-flow statistics were realized by adjusting the network structure, optimizing the loss function, and introducing weight regularization. This model, which we call YOLO-UA, was initialized based on the weight of a YOLO model pre-trained while using the VOC2007 data set. The UA-CAR data set with complex

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Dayes, Joseph, Chandran M. Ajay, K. Akshay, K. Aswathy, and P. Vijina. "Violence Detection in Real Time for Surveillance." Research and Reviews: Advancement in Cyber Security 2, no. 1 (2024): 15–26. https://doi.org/10.5281/zenodo.14160132.

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<em>In an interconnected world, the need for advanced surveillance systems capable of detecting criminal offenses and violence is necessary. This paper aims to create a real-time surveillance system using the latest available technology, which focuses on the detection of criminal actions like human fights from surveillance camera footage. The paper makes use of YOLO (You Only Look Once) model, an advanced ML method, which is known for its speed and accuracy in object and action detection. The system employs alert mechanisms to respond to suspicious activities. It extracts its frames and distin

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Pratama, Bagus Kurniawan, Sri Lestanti, and Yusniarsi Primasari. "Implementasi Algoritma You Only Look Once (YOLO) untuk Mendeteksi Bahasa Isyarat SIBI." ProTekInfo(Pengembangan Riset dan Observasi Teknik Informatika) 11, no. 2 (2024): 7–14. http://dx.doi.org/10.30656/protekinfo.v11i2.9105.

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The Indonesian Sign Language System (SIBI) is a translator of sign language into text or speech. This research aims to bridge communication between ordinary people and speech impaired people through the introduction of SIBI sign language using the YOLO algorithm. This research uses 24 alphabets which are divided into 4 groups, where each alphabet has 20 image data which is divided into 70% train data, 25% valid data, and 5% test data. The train data was then added with augmented data from Roboflow which was then carried out using a training process using a batch number of 16 and epochs of 100.

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Sarosa, Moechammad, and Nailul Muna. "Implementasi Algoritma You Only Look Once (YOLO) untuk Deteksi Korban Bencana Alam." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 4 (2021): 787. http://dx.doi.org/10.25126/jtiik.2021844407.

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<p class="Abstrak">Bencana alam merupakan suatu peristiwa yang dapat menyebabkan kerusakan dan menciptakan kekacuan. Bangunan yang runtuh dapat menyebabkan cidera dan kematian pada korban. Lokasi dan waktu kejadian bencana alam yang tidak dapat diprediksi oleh manusia berpotensi memakan korban yang tidak sedikit. Oleh karena itu, untuk mengurangi korban yang banyak, setelah kejadian bencana alam, pertama yang harus dilakukan yaitu menemukan dan menyelamatkan korban yang terjebak. Penanganan evakuasi yang cepat harus dilakukan tim SAR untuk membantu korban. Namun pada kenyataannya, tim SA

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Jannah, Zuanita Syifaul, and Felix Andreas Sutanto. "Implementasi Algoritma YOLO (You Only Look Once) Untuk Deteksi Rias Adat Nusantara." Jurnal Ilmiah Universitas Batanghari Jambi 22, no. 3 (2022): 1490. http://dx.doi.org/10.33087/jiubj.v22i3.2421.

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Makeup and fashion are important things that need to be considered by the future bride and groom. Traditional bridal makeup and dress in Indonesia have certain meanings and symbols according to the region and beliefs of their ancestors. In general, the meaning of the symbol is prayers and hopes that the home life to be lived will always be endowed with happiness and well-being. One way to distinguish the types of traditional makeup can be observed through the form of makeup on the face, the order and accessories used on the bride's hair. Current technological developments can be used for the i

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Pratama, Yovi, and Errissya Rasywir. "Eksperimen Penerapan Sistem Traffic Counting dengan Algoritma YOLO (You Only Look Once) V.4." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 4 (2021): 1438. http://dx.doi.org/10.30865/mib.v5i4.3309.

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Traffic counting is the activity of counting traffic (vehicles) that pass on the road in a certain period. The purpose of traffic counting is to collect traffic data, determine traffic characteristics, determine vehicle composition and measure traffic performance. With the YOLO V.4 algorithm, changes in the position, size and volume of the detected object can be carried out in several tests. Although not all the results of using this algorithm are perfect on all data, the results tend to be good. This is related to the services provided in the form of a convolutional layer on YOLO reducing dow

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Yang, Yang, and Hongmin Deng. "GC-YOLOv3: You Only Look Once with Global Context Block." Electronics 9, no. 8 (2020): 1235. http://dx.doi.org/10.3390/electronics9081235.

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In order to make the classification and regression of single-stage detectors more accurate, an object detection algorithm named Global Context You-Only-Look-Once v3 (GC-YOLOv3) is proposed based on the You-Only-Look-Once (YOLO) in this paper. Firstly, a better cascading model with learnable semantic fusion between a feature extraction network and a feature pyramid network is designed to improve detection accuracy using a global context block. Secondly, the information to be retained is screened by combining three different scaling feature maps together. Finally, a global self-attention mechani

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Abdul Hadi, Muhammad, Rian Ferdian, and Lathifah Arief. "Klasifikasi Tingkat Ancaman Kriminalitas Bersenjata Menggunakan Metode You Only Look Once (YOLO)." CHIPSET 2, no. 01 (2021): 33–40. http://dx.doi.org/10.25077/chipset.2.01.33-40.2021.

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The research aim to recognize potential weapon threats through object detection on camera. This research utilize YOLO (You Only Look Once) method in object detection which implemented on Raspberry Pi 4. The process was by detecting object from the camera and classify the object class in 2 available classes : Gun and Knife. Meanwhile, in the classifying process, it also count the object in every classes. When the system detect object in the process, it will send notification in terms of threat level through android application so that the user or operator can mitigate the threat immediately. Fr

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Kamaru Zaman, Fadhlan Hafizhelmi, Syahrul Afzal Che Abdullah, Noorfadzli Abdul Razak, Juliana Johari, Idnin Pasya, and Khairil Anwar Abu Kassim. "Visual-Based Motorcycle Detection using You Only Look Once (YOLO) Deep Network." IOP Conference Series: Materials Science and Engineering 1051, no. 1 (2021): 012004. http://dx.doi.org/10.1088/1757-899x/1051/1/012004.

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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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Wiliani, Ninuk, Anita Putri Valeria Dhiu Lusi, and Nur Hikmah. "Identifying Skin Cancer Disease Types With You Only Look Once (YOLO) Algorithm." Jurnal Riset Informatika 5, no. 3 (2023): 455–64. http://dx.doi.org/10.34288/jri.v5i3.241.

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The skin is the outermost vital organ and is susceptible to various diseases, including skin cancer. The number of cases of skin cancer around the world continues to increase every year, including in Indonesia. Proper handling is critical to cure skin cancer, and one of the solutions that can be used is the Deep Learning method. This study aims to apply the Deep Learning method, specifically an object detection algorithm called You Only Look Once (YOLO), for early skin cancer detection. The YOLOv5s algorithm is the model for this study because it is accurate and can detect objects in real-time

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Tella, Hambal, Mohamed Mohandes, B. Liu, Ali Al-Shaikhi, and Shafiqur Rehman. "A novel cost-function for transformerbased YOLO algorithm to detect photovoltaic panel defects." FME Transactions 52, no. 4 (2024): 639–46. http://dx.doi.org/10.5937/fme2404639t.

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Solar panel defects can lead to substantial efficiency loss and increased maintenance expenses. Conventional defect detection methods are often slow and ineffective. Thisstudy revisits the You Only Look Once (YOLO) algorithm and its variations, assessing their efficacy in identifying defects in thermal images of solar panels. Subsequently, we introduce a novel YOLO algorithm, termed YOLOS-PV, built uponthe transformer-based YOLOS algorithm. The proposed algorithm introduces newloss function weights to prioritize localized objects and visualize the attention mapof each transformer head within t

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Honnainah, Honainah, and Ratri Enggar Pawening. "Deteksi Otomatis Terhadap Pelanggaran Pembuang Sampah Menggunakan Metode You Only Look Once (YOLO)." TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora 4, no. 2 (2023): 98–105. http://dx.doi.org/10.33650/trilogi.v4i2.6676.

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Disposing of garbage is a bad thing that can spoil the view, cause bad smells, cause low to high level flooding, cause various diseases and can pollute the environment. Even though the ban on disposing of trash has been implemented, there are still many who violate it. The importance of avoiding this makes a study aimed at automatically detecting violations of waste disposal. The method used is YOLOv5, this method is an algorithm that can identify objects with high accuracy, besides that it can also carry out tracking processes in the form of bounding boxes for objects in real time. The progra

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Saputra, Dede Haris, Bahtiar Imran, and Juhartini. "OBJECT DETECTION UNTUK MENDETEKSI CITRA BUAH-BUAHAN MENGGUNAKAN METODE YOLO." Jurnal Kecerdasan Buatan dan Teknologi Informasi 2, no. 2 (2023): 70–80. http://dx.doi.org/10.69916/jkbti.v2i2.18.

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Perkembangan ilmu pengetahuan dan teknologi dalam Artificial Intelligence yang sangat pesat saat ini, telah membawa perubahan yang sangat pesat pula dalam berbagai aspek kehidupan. Terutama kecerdasan buatan merupakan sebuah teknologi komputer atau mesin yang memiliki kecerdasan layaknya manusia. Sederhananya sebuah instruksi pintar yang diberikan kepada program maupun mesin, salah satunya yaitu Object Detection untuk mendeteksi citra buah menggunakan You Only Look Once (YOLO). Metode yang dapat digunakan untuk pengenalan objek pada citra buah adalah Deep Learning. You Only Look Once (YOLO) me

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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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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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Sari, Diana Puspita, and A. Haidar Mirza. "THE DETECTION OF FACE RECOGNITION AS EMPLOYEE ATTENDANCE PRESENCE USING THE YOLO ALGORITHM (YOU ONLY LOOK ONCE)." Jurnal Darma Agung 30, no. 3 (2022): 41. http://dx.doi.org/10.46930/ojsuda.v30i3.2187.

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face detection is a fundamental and important process in the field of facial recognition. The aim of this face detection was to determine the presence and mark the position of the face through an image called a bounding box. The problem that we often encounter in attendance machines is that it is often difficult and takes a few seconds to perform facial recognition with a low level of accuracy. Therefore, an attendance system that is faster and more accurate in recognizing faces is needed with the aim of increasing better accuracy. This research was undertaken by applying the You Only Look Onc

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Zheng, Xin, Songrong Qian, Shaodong Wei, Shiyun Zhou, and Yi Hou. "The Combination of Transformer and You Only Look Once for Automatic Concrete Pavement Crack Detection." Applied Sciences 13, no. 16 (2023): 9211. http://dx.doi.org/10.3390/app13169211.

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The real-time detection of cracks is an important part of road maintenance and an important initiative to reduce traffic accidents caused by road cracks. In response to the lack of efficiency of current research results for the real-time detection of road cracks and the low storage and computational capacity of edge devices, a new automatic crack detection algorithm is proposed: BT–YOLO. We combined Bottleneck Transformer with You Only Look Once (YOLO), which is more conducive to extracting the features of small cracks than YOLOv5s. The introduction of DWConv to the feature extraction network

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Rohaan,, Khan. "Helmet Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41490.

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This paper presents a real-time helmet detection system designed to enhance safety in workplaces and traffic environments. Utilizing deep learning models like YOLO (You Only Look Once), the system detects whether individuals are wearing helmets in live video feeds or images. Trained on a diverse dataset, the system achieves high accuracy and efficiency, making it suitable for deployment in safetycritical areas. The paper highlights its potential applications, challenges, and future improvements, such as IoT integration and edge computing, to further enhance performance and scalability. This wo

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