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Journal articles on the topic 'Yoco'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2025

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1

Belaunde, Luisa Elvira, and Juan Alvaro Echeverri. "El yoco del cielo es cultivado: perspectivas sobre Paullinia yoco en el chamanismo airo-pai (secoya-tucano occidental)." Anthropologica 26, no. 26 (2008): 87–111. http://dx.doi.org/10.18800/anthropologica.200801.004.

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Este artículo asocia la cosmovisón chamánica y onírica de los airo-pai (secoya) con su manejo silvicultural del yoco (Paullinia yoco), una liana silvestre rica en cafeína, endémica del refugio del pleistocénico del Napo. Según las nociones etnobotánicas airo-pai,el yoco tiene la propiedad de «dar consejo» a quienes lo consumen. A partir del análisis de un canto chamánico de yajé (Banisteriopsis caapi), mitos e interpretación de sueños, mostramos cómo el manejo de esta especie es concebido según perspectivas contrastantes: para los espíritus celestiales el yoco es una planta cultivada, que crec

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Tornay-Márquez, M. Cruz. "Revalorización cultural e identitaria de mujeres afrodescendientes e indígenas en radios comunitarias." Chasqui. Revista Latinoamericana de Comunicación 140 (April 7, 2019): 163–78. https://doi.org/10.16921/chasqui.v0i140.3877.

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En este artículo se presentan resultados relacionados con la revalorización cultural e identitaria identificados en dos experiencias de radios comunitarias gestionadas por mujeres afrodescendientes de Venezuela e indígenas de la sierra central de Ecuador, pertenecientes a poblaciones excluidas tanto de la representación como del acceso a los medios de comunicación. Se toman como objeto de análisis las experiencias de la radio Avanzadora de Yoco y el programa <em>Alli Kawsaipak Jampikuna/ Medicina para el Buen Vivir</em>grabado en la emisora comuni

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Nguyen, Quoc Toan, and Tang Quang Hieu. "Enhancing Single-Image Super-Resolution using Patch-Mosaic Data Augmentation on Lightweight Bimodal Network." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 10, no. 2 (2023): e1. http://dx.doi.org/10.4108/eetinis.v10i2.2774.

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With the advancement of deep learning, single-image super-resolution (SISR) has made significant strides. However, most current SISR methods are challenging to employ in real-world applications because they are doubtlessly employed by substantial computational and memory costs caused by complex operations. Furthermore, an efficient dataset is a key factor for bettering model training. The hybrid models of CNN and Vision Transformer can be more efficient in the SISR task. Nevertheless, they require substantial or extremely high-quality datasets for training that could be unavailable from time t

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Jorge, Gómez Rendón, and Piaguaje Lucitante Justino. "El arte verbal de las fórmulas y los cantos sagrados siekopai." INPC 1, no. 1 (2023): e1. https://doi.org/10.5281/zenodo.8335676.

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Los siekopai son una de las catorce nacionalidades del Ecuador. En el país se encuentran repartidos en siete comunidades de la provincia de Sucumbíos. Aunque su lengua ancestral, el paikoka, muestra al momento un alto grado de vitalidad, el vertiginoso bilingüismo observado entre las generaciones jóvenes, junto con un acelerado proceso de aculturación provocado por la urbanización y la migración a las ciudades, auguran un desplazamiento lingüístico hacia el castellano en las próximas generaciones. Dentro de las expresiones del p

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Wu, Donghua, Zhongmin Qian, Dongyang Wu, and Junling Wang. "FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network." Forests 15, no. 5 (2024): 787. http://dx.doi.org/10.3390/f15050787.

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Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances,

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Dai, Wei, Zhengjun Zhai, Dezhong Wang, et al. "YOMO-Runwaynet: A Lightweight Fixed-Wing Aircraft Runway Detection Algorithm Combining YOLO and MobileRunwaynet." Drones 8, no. 7 (2024): 330. http://dx.doi.org/10.3390/drones8070330.

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The runway detection algorithm for fixed-wing aircraft is a hot topic in the field of aircraft visual navigation. High accuracy, high fault tolerance, and lightweight design are the core requirements in the domain of runway feature detection. This paper aims to address these needs by proposing a lightweight runway feature detection algorithm named YOMO-Runwaynet, designed for edge devices. The algorithm features a lightweight network architecture that follows the YOMO inference framework, combining the advantages of YOLO and MobileNetV3 in feature extraction and operational speed. Firstly, a l

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Pérez, Diana, and José Iannacone. "EFECTO INSECTICIDA DE SACHA YOCO (Paullinia clavigera var. bullata Simpson) (SAPINDACEAE) Y OREJA DE TIGRE (Tradescantia zebrina Hort ex Bosse) (COMMELINACEAE) EN EL CONTROL DE Anopheles benarrochi Gabaldon, Cova García y López, 1941, PRINCIPAL VECTOR DE." Ecología Aplicada 3, no. 1-2 (2016): 64. http://dx.doi.org/10.21704/rea.v3i1-2.272.

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Orozco-Arias, Simon, Luis Humberto Lopez-Murillo, Johan S. Piña, et al. "Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks." PLOS ONE 18, no. 9 (2023): e0291925. http://dx.doi.org/10.1371/journal.pone.0291925.

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Analysis of eukaryotic genomes requires the detection and classification of transposable elements (TEs), a crucial but complex and time-consuming task. To improve the performance of tools that accomplish these tasks, Machine Learning approaches (ML) that leverage computer resources, such as GPUs (Graphical Processing Unit) and multiple CPU (Central Processing Unit) cores, have been adopted. However, until now, the use of ML techniques has mostly been limited to classification of TEs. Herein, a detection-classification strategy (named YORO) based on convolutional neural networks is adapted from

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Venkatesh, Satvik, David Moffat, and Eduardo Reck Miranda. "You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection." Applied Sciences 12, no. 7 (2022): 3293. http://dx.doi.org/10.3390/app12073293.

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Audio segmentation and sound event detection are crucial topics in machine listening that aim to detect acoustic classes and their respective boundaries. It is useful for audio-content analysis, speech recognition, audio-indexing, and music information retrieval. In recent years, most research articles adopt segmentation-by-classification. This technique divides audio into small frames and individually performs classification on these frames. In this paper, we present a novel approach called You Only Hear Once (YOHO), which is inspired by the YOLO algorithm popularly adopted in Computer Vision

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Ando, Y. "Expression of a small RNA, BS203 RNA, from the yocI–yocJ intergenic region of Bacillus subtilis genome." FEMS Microbiology Letters 207, no. 1 (2002): 29–33. http://dx.doi.org/10.1016/s0378-1097(01)00551-1.

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Iteen, Alexander, Eric J. Koch, Amanda Wojahn, et al. "Feasibility of Obtaining Intraosseous and Intravenous Access Using Night Vision Goggle Focusing Adaptors." Journal of Special Operations Medicine 22, no. 1 (2022): 56. http://dx.doi.org/10.55460/we0q-yoca.

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Bartet, Daniel. "Yodo." Educación Química 13, no. 1 (2018): 69. http://dx.doi.org/10.22201/fq.18708404e.2002.1.66322.

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<span>Desde mediados del siglo XIX, Chile es el más importante productor mundial de yodo, con una participación del 51% del mercado mundial. Sus yacimientos, las calicheras, se encuentran en la región más septentrional del país, las cuales contiene yodo en forma de yodato de potasio. Otras fuentes de yodo son las algas marinas, particularmente las del género Laminaria, en las que el contenido de yodo, en forma de yoduro de sodio, representa el 0,45% de la masa de algas seca; el agua de mar, con una concentración 4 ´ 10--7 mol/L. Aunque mayoritariamente se encuentra como yoduros solubles,

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Chuculate, Eddie D. "Yoyo." Iowa Review 31, no. 1 (2001): 61–77. http://dx.doi.org/10.17077/0021-065x.5366.

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Bohn Stafleu van Loghum. "YOLO!" PodoPost 31, no. 1 (2018): 13. http://dx.doi.org/10.1007/s12480-018-0003-0.

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Oreski, Goran. "YOLO*C — Adding context improves YOLO performance." Neurocomputing 555 (October 2023): 126655. http://dx.doi.org/10.1016/j.neucom.2023.126655.

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Kadhum, Aseil Nadhum, and Aseel Nadhum Kadhum. "Literature Survey on YOLO Models for Face Recognition in Covid-19 Pandemic." June-July 2023, no. 34 (July 29, 2023): 27–35. http://dx.doi.org/10.55529/jipirs.34.27.35.

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Artificial Intelligence and robotics the fields in which there is necessary required object detection algorithms. In this study, YOLO and different versions of YOLO are studied to find out advantages of each model as well as limitations of each model. Even in this study, YOLO version similarities and differences are studied. Improvement in the YOLO (You Only Look Once) as well as CNN (Convolutional Neural Network) is the research study present going on for different object detection. In this paper, each YOLO version model is discussed in detail with advantages, limitations and performance. YOL

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Arden, Caroline. "Yolo County." Colorado Review 38, no. 2 (2011): 53–72. http://dx.doi.org/10.1353/col.2011.0039.

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Dimitrakaki, Angela. "Yoko Ono." Third Text 12, no. 42 (1998): 99–104. http://dx.doi.org/10.1080/09528829808576725.

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Lashua, Brett D. "DWYL? YOLO..." Annals of Leisure Research 17, no. 2 (2014): 121–26. http://dx.doi.org/10.1080/11745398.2014.920761.

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Wu, YiHeng, Jiaqiang Dong, and Jianxin Cheng. "YOLO-DCNet." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–23. http://dx.doi.org/10.4018/ijswis.339000.

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Enhanced processors empower edge devices like smartphones for human detection, yet their application is constrained by algorithmic efficiency and precision. This paper introduces YOLO-DCNet, a lightweight neural network detector built upon YOLOv7-tiny. Incorporating a dynamic multi-head structural re-parameterization (DMSR) module within its backbone network enables effective processing of the features utilized in the model. To improve multi-scale feature aggregation, the model integrates a channel information compression and linear mapping (CLM) module into its feature pyramid architecture. M

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Wang, Chao, Qijin Wang, Yu Qian, Yating Hu, Ying Xue, and Hongqiang Wang. "DP-YOLO: Effective Improvement Based on YOLO Detector." Applied Sciences 13, no. 21 (2023): 11676. http://dx.doi.org/10.3390/app132111676.

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YOLOv5 remains one of the most widely used real-time detection models due to its commendable performance in accuracy and generalization. However, compared to more recent detectors, it falls short in label assignment and leaves significant room for optimization. Particularly, recognizing targets with varying shapes and poses proves challenging, and training the detector to grasp such features requires expert verification or collective discussion during the dataset labeling process, especially in domain-specific contexts. While deformable convolutions offer a partial solution, their extensive us

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Маратулы, А., and Е. А. Абибуллаев. "PERFORMANCE STUDY AND COMPARATIVE ANALYSIS OF YOLO-NAS AND PREVIOUS VERSIONS OF YOLO." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 5, no. 1(17) (2024): 71–83. http://dx.doi.org/10.54309/ijict.2024.17.1.006.

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Исследование посвящено анализу производительности алгоритма обнаружения объектов YOLO-NAS (You Only Look Once – Neural Architecture Search) в сравнении с его предшественниками из семейства YOLO. Целью работы является оценка эффективности YOLO-NAS и выявление его преимуществ и недостатков по сравнению с предыдущими версиями алгоритма YOLO. Исследование проводится в нескольких ключевых аспектах производительности, включая скорость обработки изображений, точность обнаружения объектов, а также эффективность использования ресурсов вычислительной системы. Для достижения этих целей, используются стан

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Liu, Chuanyang, Yiquan Wu, Jingjing Liu, Zuo Sun, and Huajie Xu. "Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model." Applied Sciences 11, no. 10 (2021): 4647. http://dx.doi.org/10.3390/app11104647.

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Insulator fault detection is one of the essential tasks for high-voltage transmission lines’ intelligent inspection. In this study, a modified model based on You Only Look Once (YOLO) is proposed for detecting insulator faults in aerial images with a complex background. Firstly, aerial images with one fault or multiple faults are collected in diverse scenes, and then a novel dataset is established. Secondly, to increase feature reuse and propagation in the low-resolution feature layers, a Cross Stage Partial Dense YOLO (CSPD-YOLO) model is proposed based on YOLO-v3 and the Cross Stage Partial

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Hwang, Hoseong, Donghyun Kim, and Hochul Kim. "FD-YOLO: A YOLO Network Optimized for Fall Detection." Applied Sciences 15, no. 1 (2025): 453. https://doi.org/10.3390/app15010453.

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Falls are defined by the World Health Organization (WHO) as incidents in which an individual unintentionally falls to the ground or a lower level. Falls represent a serious public health issue, ranking as the second leading cause of death from unintentional injuries, following traffic accidents. While fall prevention is crucial, prompt intervention after a fall is equally necessary. Delayed responses can result in severe complications, reduced recovery potential, and a negative impact on quality of life. This study focuses on detecting fall situations using image-based methods. The fall images

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Wang, Yujin, Xueying Lin, Zhaowei Xiang, and Wen-Hao Su. "VM-YOLO: YOLO with VMamba for Strawberry Flowers Detection." Plants 14, no. 3 (2025): 468. https://doi.org/10.3390/plants14030468.

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Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberr

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Wei, Jian, Qinzhao Wang, and Zixu Zhao. "YOLO-G: Improved YOLO for cross-domain object detection." PLOS ONE 18, no. 9 (2023): e0291241. http://dx.doi.org/10.1371/journal.pone.0291241.

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Cross-domain object detection is a key problem in the research of intelligent detection models. Different from lots of improved algorithms based on two-stage detection models, we try another way. A simple and efficient one-stage model is introduced in this paper, comprehensively considering the inference efficiency and detection precision, and expanding the scope of undertaking cross-domain object detection problems. We name this gradient reverse layer-based model YOLO-G, which greatly improves the object detection precision in cross-domain scenarios. Specifically, we add a feature alignment b

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Peng, Bo, and Tae-Kook Kim. "YOLO-HF: Early Detection of Home Fires Using YOLO." IEEE Access 13 (2025): 79451–66. https://doi.org/10.1109/access.2025.3566907.

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Reddy, Sarah, Suleyman Yasin Goksu, Nivan Chowattukunnel, et al. "Characteristics and trends in treatment utilization in young-onset cholangiocarcinoma." Journal of Clinical Oncology 40, no. 4_suppl (2022): 386. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.386.

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386 Background: Although the incidence of cholangiocarcinoma is rising, little is known about the demographic and clinical characteristics of patients with young-onset cholangiocarcinoma (YOCC), diagnosed at age < 50 years. The objective of this study was to compare patients with YOCC, and patients with typical-onset cholangiocarcinoma (TOCC; diagnosed age >50 years), and to describe trends in treatment utilization and factors associated with survival in YOCC. Methods: We identified patients diagnosed with intrahepatic cholangiocarcinoma, extrahepatic cholangiocarcinoma, and hilar cholan

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Tao, Wenlei. "Analysis the improvements of YOLOv5 algorithms: NRT-YOLO, MR-YOLO and YPH-YOLOv5." Applied and Computational Engineering 54, no. 1 (2024): 155–60. http://dx.doi.org/10.54254/2755-2721/54/20241466.

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Computer Vision (CV) is a fundamental aspect of artificial intelligence, with applications spanning multiple domains. The YOLO (You Only Look Once) algorithm has significantly contributed to real-time object recognition in CV. This paper explores the evolution of the YOLO algorithm, focusing on the improvements brought by three specialized variants: NRT-YOLO, MR-YOLO, and TPH-YOLOv5. NRT-YOLO addresses the challenge by introducing the C3NRT module, enhancing precision while maintaining low complexity. MR-YOLO optimizes YOLOv5 for industrial quality control, improving speed and accuracy. TPH-YO

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Wang, Mingxun, Baolu Yang, Xin Wang, et al. "YOLO-T: Multitarget Intelligent Recognition Method for X-ray Images Based on the YOLO and Transformer Models." Applied Sciences 12, no. 22 (2022): 11848. http://dx.doi.org/10.3390/app122211848.

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X-ray security inspection processes have a low degree of automation, long detection times, and are subject to misjudgment due to occlusion. To address these problems, this paper proposes a multi-objective intelligent recognition method for X-ray images based on the YOLO deep learning network and an optimized transformer structure (YOLO-T). We also construct the GDXray-Expanded X-ray detection dataset, which contains multiple types of dangerous goods. Using this dataset, we evaluated several versions of the YOLO deep learning network model and compared the results to those of the proposed YOLO-

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Liu, Jiayi, Xingfei Zhu, Xingyu Zhou, Shanhua Qian, and Jinghu Yu. "Defect Detection for Metal Base of TO-Can Packaged Laser Diode Based on Improved YOLO Algorithm." Electronics 11, no. 10 (2022): 1561. http://dx.doi.org/10.3390/electronics11101561.

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Defect detection is an important part of the manufacturing process of mechanical products. In order to detect the appearance defects quickly and accurately, a method of defect detection for the metal base of TO-can packaged laser diode (metal TO-base) based on the improved You Only Look Once (YOLO) algorithm named YOLO-SO is proposed in this study. Firstly, convolutional block attention mechanism (CBAM) module was added to the convolutional layer of the backbone network. Then, a random-paste-mosaic (RPM) small object data augmentation module was proposed on the basis of Mosaic algorithm in YOL

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Munroe, Alexandra, and John Hendricks. "Yes Yoko Ono." Woman's Art Journal 24, no. 1 (2003): 54. http://dx.doi.org/10.2307/1358819.

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Laumond, Jean–Paul, Mehdi Benallegue, Justin Carpentier, and Alain Berthoz. "The Yoyo-Man." International Journal of Robotics Research 36, no. 13-14 (2017): 1508–20. http://dx.doi.org/10.1177/0278364917693292.

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The paper reports on two results issued from a multidisciplinary research action exploring the motor synergies of anthropomorphic walking. By combining the biomechanical, neurophysiology, and robotics perspectives, it is intended to better understand human locomotion with the ambition to better design bipedal robot architectures. The motivation of the research starts from the simple observation that humans may stumble when following a simple reflex-based locomotion on uneven terrains. The rationale combines two well established results in robotics and neuroscience, respectively: passive robot

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Concannon, Kevin. "Yoko Ono's Dreams." Performance Research 19, no. 2 (2014): 103–8. http://dx.doi.org/10.1080/13528165.2014.928525.

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Kaushal, Manisha. "Rapid -YOLO: A novel YOLO based architecture for shadow detection." Optik 260 (June 2022): 169084. http://dx.doi.org/10.1016/j.ijleo.2022.169084.

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Wang, Feilong, Xiaobing Yang, and Juan Wei. "YOLO-ESL: An Enhanced Pedestrian Recognition Network Based on YOLO." Applied Sciences 14, no. 20 (2024): 9588. http://dx.doi.org/10.3390/app14209588.

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Pedestrian detection is a critical task in computer vision; however, mainstream algorithms often struggle to achieve high detection accuracy in complex scenarios, particularly due to target occlusion and the presence of small objects. This paper introduces a novel pedestrian detection algorithm, YOLO-ESL, based on the YOLOv7 framework. YOLO-ESL integrates the ELAN-SA module, designed to enhance feature extraction, with the LGA module, which improves feature fusion. The ELAN-SA module optimizes the flexibility and efficiency of small object feature extraction, while the LGA module effectively i

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Dai, Yuan, Weiming Liu, Heng Wang, Wei Xie, and Kejun Long. "YOLO-Former: Marrying YOLO and Transformer for Foreign Object Detection." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–14. http://dx.doi.org/10.1109/tim.2022.3219468.

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Yan, Bingnan, Jiaxin Li, Zhaozhao Yang, Xinpeng Zhang, and Xiaolong Hao. "AIE-YOLO: Auxiliary Information Enhanced YOLO for Small Object Detection." Sensors 22, no. 21 (2022): 8221. http://dx.doi.org/10.3390/s22218221.

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Small object detection is one of the key challenges in the current computer vision field due to the low amount of information carried and the information loss caused by feature extraction. You Only Look Once v5 (YOLOv5) adopts the Path Aggregation Network to alleviate the problem of information loss, but it cannot restore the information that has been lost. To this end, an auxiliary information-enhanced YOLO is proposed to improve the sensitivity and detection performance of YOLOv5 to small objects. Firstly, a context enhancement module containing a receptive field size of 21×21 is proposed, w

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Zheng, Xuwen, Zhiwei Zhou, and Chonlatee Photong. "GCSEM-YOLO small scale enhanced face detector based on YOLO." Edelweiss Applied Science and Technology 9, no. 3 (2025): 840–55. https://doi.org/10.55214/25768484.v9i3.5356.

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Face detection is a crucial aspect of computer vision, often challenged by factors such as varying scales, occlusions, and diverse facial features. In this study, we introduce GCSEM-YOLO, an innovative real-time face detection method built upon the YOLOv8 architecture. This approach incorporates a novel feature extraction module (GCSEM) alongside a specialized small-scale detection head, designed to capture pixel information across multiple levels and enhance the receptive field, thereby improving the accuracy of small face detection. To address the imbalance between easy and difficult samples

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Yu, Qifeng, Yudong Han, Xinjia Gao, Wuguang Lin, and Yi Han. "Comparative Analysis of Improved YOLO v5 Models for Corrosion Detection in Coastal Environments." Journal of Marine Science and Engineering 12, no. 10 (2024): 1754. http://dx.doi.org/10.3390/jmse12101754.

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Coastal areas face severe corrosion issues, posing significant risks and economic losses to equipment, personnel, and the environment. YOLO v5, known for its speed, accuracy, and ease of deployment, has been employed for the rapid detection and identification of marine corrosion. However, corrosion images often feature complex characteristics and high variability in detection targets, presenting significant challenges for YOLO v5 in recognizing and extracting corrosion features. To improve the detection performance of YOLO v5 for corrosion image features, this study investigates two enhanced m

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Liu, Chuanyang, Yiquan Wu, Jingjing Liu, and Jiaming Han. "MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images." Energies 14, no. 5 (2021): 1426. http://dx.doi.org/10.3390/en14051426.

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Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite i

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Dewi, Christine, and Henoch Juli Christanto. "Combination of Deep Cross-Stage Partial Network and Spatial Pyramid Pooling for Automatic Hand Detection." Big Data and Cognitive Computing 6, no. 3 (2022): 85. http://dx.doi.org/10.3390/bdcc6030085.

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The human hand is involved in many computer vision tasks, such as hand posture estimation, hand movement identification, human activity analysis, and other similar tasks, in which hand detection is an important preprocessing step. It is still difficult to correctly recognize some hands in a cluttered environment because of the complex display variations of agile human hands and the fact that they have a wide range of motion. In this study, we provide a brief assessment of CNN-based object identification algorithms, specifically Densenet Yolo V2, Densenet Yolo V2 CSP, Densenet Yolo V2 CSP SPP,

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Rosa Andrie Asmara, M. Rahmat Samudra, and Dimas Wahyu Wibowo. "IDENTIFIKASI PERSON PADA GAME FIRST PERSON SHOOTER (FPS) MENGGUNAKAN YOLO OBJECT DETECTION DAN DIIMPLEMENTASIKAN SEBAGAI AGENT CERDAS AUTOMATIC TARGET HIT." Jurnal Teknik Ilmu Dan Aplikasi 3, no. 2 (2022): 141–45. http://dx.doi.org/10.33795/jtia.v3i1.87.

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Game First-person shooter (FPS) merupakan genre video game yang berpusat pada senjata, umumnya permainan ini membutuhkan akurasi untuk membidik sasaran dengan cepat. Tetapi pemain terutama pemain baru biasanya tidak memiliki reaksi yang cepat dalam mengetahui lawan(person) disekitarnya. Metode yang biasa digunakan adalah memanipulasi memori menggunakan dynamic-link library untuk membuat asisten bidik untuk mendeteksi lawan. Penelitian ini mendeteksi dan mengklasifikasi citra person dengan menggunakan metode YOLO. Versi YOLO yang digunakan sebanyak tiga versi, yaitu YOLOv3, YOLOv4 dan YOLOv5s,

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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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Zhou, Xuan, Jianping Yi, Guokun Xie, Yajuan Jia, Genqi Xu, and Min Sun. "Human Detection Algorithm Based on Improved YOLO v4." Information Technology and Control 51, no. 3 (2022): 485–98. http://dx.doi.org/10.5755/j01.itc.51.3.30540.

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The human behavior datasets have the characteristics of complex background, diverse poses, partial occlusion, and diverse sizes. Firstly, this paper adopts YOLO v3 and YOLO v4 algorithms to detect human objects in videos, and qualitatively analyzes and compares detection performance of two algorithms on UTI, UCF101, HMDB51 and CASIA datasets. Then, this paper proposed an improved YOLO v4 algorithm since the vanilla YOLO v4 has incomplete human detection in specific video frames. Specifically, the improved YOLO v4 introduces the Ghost module in the CBM module to further reduce the number of par

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Xu, Danqing, and Yiquan Wu. "MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection." Remote Sensing 12, no. 19 (2020): 3118. http://dx.doi.org/10.3390/rs12193118.

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High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreove

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Azizah, Nur, Yoga Sahria, Sahwari Sahwari, and Muhaimin Iskandar. "CAR VEHICLE IMAGE OBJECT DETECTION USING YOU ONLY LIVE ONCE (YOLO)." Anterior Jurnal 22, no. 3 (2023): 211–16. http://dx.doi.org/10.33084/anterior.v22i3.5577.

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This research aims to analyze the performance of image object detection methods using You Only Live Once (YOLO) specifically in the context of car detection. YOLO-based object detection methods have gained great attention in the artificial intelligence community due to their ability to perform real-time object detection. In this research, we focus on using YOLO to detect car objects in images. The YOLO method will be tested for performance using a dataset of car images that have been collected from various sources. This dataset includes various lighting conditions, backgrounds, and car positio

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Park, Jungsu, Jiwon Baek, Jongrack Kim, Kwangtae You, and Keugtae Kim. "Deep Learning-Based Algal Detection Model Development Considering Field Application." Water 14, no. 8 (2022): 1275. http://dx.doi.org/10.3390/w14081275.

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Algal blooms have various effects on drinking water supply systems; thus, proper monitoring is essential. Traditional visual identification using a microscope is a time-consuming method and requires extensive labor. Recently, advanced machine learning algorithms have been increasingly applied for the development of object detection models. The You-Only-Look-Once (YOLO) model is a novel machine learning algorithm used for object detection; it has been continuously improved in newer versions, and a tiny version of each standard model presented. The tiny versions applied a less complicated archit

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Zhang, Yuan. "YOLO Series Target Detection Technology and Application." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 841–47. http://dx.doi.org/10.54097/hset.v39i.6653.

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Recently, YOLO is the most popular algorithm in machine learning. The algorithm has developed rapidly, and there are several versions at present. Each version of the framework is different, and they also have their own application areas. And maybe in one area, not only one version can be used. This paper summarizes the process of target detection, the structures of YOLO network. In addition, this work also analyzed the development, advantages and disadvantages of YOLO target detection. Finally, the application of YOLO in automatic driving and UAV detection are discussed. YOLO may develop faste

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Ye, Jing, Zhaoyu Yuan, Cheng Qian, and Xiaoqiong Li. "CAA-YOLO: Combined-Attention-Augmented YOLO for Infrared Ocean Ships Detection." Sensors 22, no. 10 (2022): 3782. http://dx.doi.org/10.3390/s22103782.

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Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction cap

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