Relevant bibliographies by topics / YOLO ALGORITHMS

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Academic literature on the topic 'YOLO ALGORITHMS'

Author: Grafiati
Published: 16 January 2024
Last updated: 31 July 2025

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Journal articles on the topic "YOLO ALGORITHMS"

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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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Liu, Liya. "Application of Multi-objective Evolutionary Algorithms for Multidimensional Sensory Data Prediction and Resource Scheduling in Smart City Design." Scalable Computing: Practice and Experience 25, no. 4 (2024): 2973–84. http://dx.doi.org/10.12694/scpe.v25i4.2929.

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Multidimensional sensory data prediction and resource scheduling are paramount challenges in the design of smart cities. This paper delves into the utilization of multi-objective evolutionary algorithms to enhance the accuracy and efficiency of target detection through optimized YOLO_v3 network models. By integrating the YOLO_v3 model with the K-means++ algorithm for Anchor_Box generation, the novel approach exhibits superior adaptability and flexibility, particularly in handling variable-sized feature pattern mappings. This adaptability better caters to the detection of targets of diverse siz
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Wan, Chengjuan, Yuxuan Pang, and Shanzhen Lan. "Overview of YOLO Object Detection Algorithm." International Journal of Computing and Information Technology 2, no. 1 (2022): 11. http://dx.doi.org/10.56028/ijcit.1.2.11.

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As an important research direction in the field of computer vision, object detection has developed 
 rapidly and many kinds of mature algorithms emerged. The series of YOLO (You Only Look Once) 
 algorithms implement one-stage detection based on regression ideas, which showing preeminent 
 in speed and owning strong generalization on a variety of datasets. This paper will give a simple 
 introduction to the current mainstream deep learning object detection algorithm, then focus on 
 combing the principle and optimizational process of the series of YOLO algorithms, summ
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Liang, Junbiao. "A review of the development of YOLO object detection algorithm." Applied and Computational Engineering 71, no. 1 (2024): 39–46. http://dx.doi.org/10.54254/2755-2721/71/20241642.

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The You Only Look Once (YOLO) algorithm series, as the forefront of object detection technology, has evolved from YOLOv1 to YOLOv10, consistently enhancing detection speed and accuracy. Through literature review and data analysis. This paper mainly discusses the development processes of the YOLO algorithm series, focuses on the changes and innovations in network structure, training strategies, and performance optimization. By introducing techniques, such as CSPNet, Anchor-free, data augmentation, and multi-scale training, the YOLO algorithm has progressively found a better balance between dete
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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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Xu, Feifei, Yan Wan, Zhipeng Ning, and Hui Wang. "Comparative Study of Lightweight Target Detection Methods for Unmanned Aerial Vehicle-Based Road Distress Survey." Sensors 24, no. 18 (2024): 6159. http://dx.doi.org/10.3390/s24186159.

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Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built b
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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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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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Liu, Tao, Bo Pang, Lei Zhang, Wei Yang, and Xiaoqiang Sun. "Sea Surface Object Detection Algorithm Based on YOLO v4 Fused with Reverse Depthwise Separable Convolution (RDSC) for USV." Journal of Marine Science and Engineering 9, no. 7 (2021): 753. http://dx.doi.org/10.3390/jmse9070753.

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Unmanned surface vehicles (USVs) have been extensively used in various dangerous maritime tasks. Vision-based sea surface object detection algorithms can improve the environment perception abilities of USVs. In recent years, the object detection algorithms based on neural networks have greatly enhanced the accuracy and speed of object detection. However, the balance between speed and accuracy is a difficulty in the application of object detection algorithms for USVs. Most of the existing object detection algorithms have limited performance when they are applied in the object detection technolo
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Kang, Shizhao, Ziyu Hu, Lianjun Liu, Kexin Zhang, and Zhiyu Cao. "Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis." Electronics 14, no. 6 (2025): 1104. https://doi.org/10.3390/electronics14061104.

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Deep-learning-based object detection algorithms play a pivotal role in various domains, including face detection, automatic driving, monitoring security, and industrial production. Compared with the traditional object detection algorithms and the two-stage object detection algorithms, the YOLO (You Only Look Once) series improved the detection speed and accuracy. In addition, the YOLO series of object detection algorithms are widely used in the industrial fields due to their real-time and high-precision characteristics. This work summarizes the main versions of YOLO series algorithms as well a
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Dissertations / Theses on the topic "YOLO ALGORITHMS"

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Marmayohan, Nivethan, and Abdirahman Farah. "Scene analysis using Tensorflow & YOLO algorithms on Raspberry pi 4." Thesis, Högskolan i Halmstad, Akademin för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45540.

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Objektdetektion är en av de viktigaste mjukvarukomponenterna i nästa generation trafikövervakning. Deep learnings-algoritmer för objektdetektion, exempelvis YOLO (You Only Look Once), är snabba och noggranna algoritmer i realtid. Realtidsdetektion och igenkänning av objekt är viktiga uppgifter för bildbehandling.  I denna studie presenteras ett inbäddat system för detektion och igenkänning av objekt i normal videohastighet (realtid). Indata är följaktligen en videoström som härstammar från en trafikmiljö i Halmstad. Hårdvaran  är Raspberry pi 4 i vilken programvarupaketen Tensorflow, YOLO  sam
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Donini, Massimo. "Algoritmi di stitching per il rilevamento dell'occupazione di aule in un contesto smart campus." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19063/.

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L'obiettivo di questo progetto è studiare un sistema con dispositivi a basso costo in grado di determinare l'occupazione corrente di un'aula, pensato principalmente per ottimizzare la gestione degli spazi universitari o di grandi edifici, implementandone un prototipo. A supporto di ciò verranno presentati algoritmi di image stitching per fornire una visione ottimale anche degli ambienti più ampi. In questo progetto è stato implementato un algoritmo di rilevamento dell'occupazione utilizzando componenti hardware economici che seguono una logica programmata appositamente. L'approccio
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ARYA, DEEPRAJ. "POTHOLE DETECTION." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20452.

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Roads are the most important form of nation's transportation system. It is extremely crucial to maintain them in good situation. Potholes are a type of road problem that can harm vehicles and have a detrimental impact on drivers' ability to drive safely, which can result in traffic accidents. Potholes that develop on the road must be filled to keep the roadways in excellent condition. It is essential that you keep them in good shape. It can be difficult to locate potholes in the road, particularly in India where there are millions of km of roadways. In a complicated road environmen
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Farinha, João Simões. "In-vehicle object detection with YOLO algorithm." Master's thesis, 2018. http://hdl.handle.net/1822/64273.

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Dissertação de mestrado em Computer Science Engineering<br>With the growing computational power that we have at our disposal and the ever-increasing amount of data available the field of machine learning has given rise to deep learning, a subset of machine learning algorithms that have shown extraordinary results in a variety of applications from natural language processing to computer vision. In the field of computer vision, these algorithms have greatly improved the state-of-the-art accuracy in tasks associated with object recognition such as detection. This thesis makes use of one of t
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Liu, Chun-Yu, and 劉峻瑜. "Implementation of Fruit Quality Classification System using YOLO Algorithm." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2chdzs.

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碩士<br>國立高雄科技大學<br>電子工程系<br>107<br>The thesis presents a proposed system that uses YOLO (You Only Look Once)-V3 algorithm, IOU (Intersection over Union) tracking method, and CNN (Convolutional Neural Network) classifier to identify the external quality of fruits. The system mainly uses the YOLO-V3 algorithm to perform the fruit detection process, uses the IOU tracking algorithm to track the designated fruits continuously, and identifies fruits during the tracking processes. It can pick up good fruits through controlling the switched gap of conveying platform. It performs the software programs o
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Yu, Wei-Chih, and 余韋志. "The object detection of moving ground vehicles using YOLO algorithm on UAV." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/qqwcjc.

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碩士<br>義守大學<br>機械與自動化工程學系<br>106<br>When people talk about the definition of Computer Vision, the first thing that comes to mind is the image classification. Previous researches illustrated that image classification is one of the most basic tasks of computer vision. However, based on the basis of image classification, there are more complicated and interesting tasks, such as: object detection, object location, image segmentation...etc in computer vision. The object detection is a practical and challenging task, which can be regarded as a combination of image classification and object location,
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Books on the topic "YOLO ALGORITHMS"

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Tovey, Craig A. A polynomial-time algorithm for computing the yolk in fixed dimension. Naval Postgraduate School, 1991.

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Book chapters on the topic "YOLO ALGORITHMS"

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Sai Venu Prathap, K., D. Srinivasulu Reddy, S. Madhusudhan, and S. Mohammed Mazharr. "Intelligent Traffic Light System Using YOLO." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1669-4_9.

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Dev Ananth, Aluri, Abhiram Seemakurthi, Sasank Tumma, and Prasanthi Boyapati. "YOLO CNN Approach for Object Detection." In Algorithms in Advanced Artificial Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003529231-72.

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Gandhi, Jimit, Purvil Jain, and Lakshmi Kurup. "YOLO Based Recognition of Indian License Plates." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_39.

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Ranjan, Ashish, Sunita Dhavale, and Suresh Kumar. "YOLO Algorithms for Real-Time Fire Detection." In Data Management, Analytics and Innovation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1414-2_40.

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Bhuyan, Hemanta Kumar, and Srihari Kalyan Nama. "Motion Feature Aggregation for Video Object Detection Using YOLO Approaches." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8398-8_14.

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Dhivya Praba, R., K. Kavitha, S. N. Shivappriya, P. Karthik, P. Kausik, and E. Dhaneshvar. "Real-Time Anomalous Event (Crime) Detection Using MGFN and YOLO." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1452-3_18.

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Santhosh Kumar, C., K. Amritha Devangana, P. L. Abirami, M. Prasanna, and S. Hari Aravind. "Identification and Classification of Skin Diseases with Erythema Using YOLO Algorithm." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-4626-6_49.

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Uddin, Mohammad Salah. "YOLO-Prayer: Prayer Posture Detection Using YOLOv5 with Multisource Data Integration." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1452-3_29.

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Prakash, Immidisetty V., and M. Palanivelan. "A Study of YOLO (You Only Look Once) to YOLOv8." In Algorithms in Advanced Artificial Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003529231-40.

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Chaves, Emmanuel Barrantes, and Ernesto Rivera Alvarado. "Terraria AI: YOLO Interface for Decision-Making Algorithms." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3556-3_1.

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Conference papers on the topic "YOLO ALGORITHMS"

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Guo, Tai, Shiyu Yang, and Ruoya Zhan. "A retinopathy detection method based on improved YOLO." In Fourth International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2024), edited by Grigorios Beligiannis and Daniel-Ioan Curiac. SPIE, 2024. http://dx.doi.org/10.1117/12.3045713.

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Jain, Rakshit, Sujal Shrivastav, Sumit Kakde, and Roshani Raut. "Performance Comparison of YOLO Algorithms in Drone Detection." In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE). IEEE, 2024. http://dx.doi.org/10.1109/icspcre62303.2024.10675042.

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Dash, Pushpita, Nisha M, Abisha G, and Kirthiga R. "Rheumatic Carditis Screening Unveiled on YOLO Algorithms Insights." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894309.

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Kumar Thakur, Mantu, and Sneha Chauhan. "Brain Tumor Detection and Classifcation Using YOLO Algorithms." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11004729.

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ZHAO, Yang, Yunxuan Zou, Mingzhen Wang, and Pinghua Yang. "Surface defects inspection of titanium alloy by using YOLO algorithm." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049481.

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Gao, Shuai, Linbo Zhang, and Tingwei Zhu. "LE-YOLO: Lightweight and Efficient Algorithm for Aerial Small Target Detection." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019728.

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Li, Shixi, BenChen Yang, Jie Kang, Xuzhao Liu, Shuai Li, and Guangbo Yi. "Research on multi-scale steel surface defects identification based on YOLO model." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang. SPIE, 2024. http://dx.doi.org/10.1117/12.3049599.

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Zhang, Zijun, ZiZhao Lin, and Xuehai Ding. "Fusion YOLO: Fusion Module Assisted Network in Detection for Automatic Target Scoring." In 2024 7th International Conference on Algorithms, Computing and Artificial Intelligence (ACAI). IEEE, 2024. https://doi.org/10.1109/acai63924.2024.10899598.

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Luo, Baohua. "Integrating Multiple Attention Mechanism Fusion Based YOLO Logistics Sorting and Detection Model." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019546.

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Qin, Tian, Dengke Chen, Jiawei Xiang, and Zhen Tian. "SP-YOLO: A Model for Detecting Solder Paste Printing-Defect in PCB." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019451.

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Reports on the topic "YOLO ALGORITHMS"

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Tovey, Craig A. A Polynomial-Time Algorithm for Computing the Yolk in Fixed Dimension. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240060.

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