Relevant bibliographies by topics / YOLOv10

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'YOLOv10'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "YOLOv10"

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Sarvesh Mahadev Shendkar. "Utilizing YOLOv10 and YOLO11 for Tomato Ripeness Detection in Vertical Farming." Advances in Nonlinear Variational Inequalities 28, no. 6s (2025): 366–79. https://doi.org/10.52783/anvi.v28.4324.

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Introduction: With the increase in urbanization, the amount of available agricultural land is constantly decreasing. Vertical farming presents one such solution to this problem, combined with new technologies, making it a more reliable and profitable approach. Objectives: This study is set to evaluate the performances of YOLOv10 and YOLOv11 models for the ripeness detection process in the harvesting of tomatoes with the aim of performing efficient and automatic harvesting. Methods: The research used an open dataset containing 667 images, divided into three classes: ripe, rotten, and unripe tom
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Sarvesh Mahadev Shendkar. "Utilizing YOLOv10 and YOLO11 for Tomato Ripeness Detection in Vertical Farming." Advances in Nonlinear Variational Inequalities 28, no. 5s (2025): 290–302. https://doi.org/10.52783/anvi.v28.3905.

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Introduction: With the increase in urbanization, the amount of available agricultural land is constantly decreasing. Vertical farming presents one such solution to this problem, combined with new technologies, making it a more reliable and profitable approach. Objectives: This study is set to evaluate the performances of YOLOv10 and YOLOv11 models for the ripeness detection process in the harvesting of tomatoes with the aim of performing efficient and automatic harvesting. Methods: The research used an open dataset containing 667 images, divided into three classes: ripe, rotten, and unripe tom
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Alkhammash, Eman H. "A Comparative Analysis of YOLOv9, YOLOv10, YOLOv11 for Smoke and Fire Detection." Fire 8, no. 1 (2025): 26. https://doi.org/10.3390/fire8010026.

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Forest fires cause extensive environmental damage, making early detection crucial for protecting both nature and communities. Advanced computer vision techniques can be used to detect smoke and fire. However, accurate detection of smoke and fire in forests is challenging due to different factors such as different smoke shapes, changing light, and similarity of smoke with other smoke-like elements such as clouds. This study explores recent YOLO (You Only Look Once) deep-learning object detection models YOLOv9, YOLOv10, and YOLOv11 for detecting smoke and fire in forest environments. The evaluat
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Yang, Xiong, Hao Wang, Qi Zhou, et al. "A Lightweight and Efficient Plant Disease Detection Method Integrating Knowledge Distillation and Dual-Scale Weighted Convolutions." Algorithms 18, no. 7 (2025): 433. https://doi.org/10.3390/a18070433.

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Plant diseases significantly undermine agricultural productivity. This study introduces an improved YOLOv10n model named WD-YOLO (Weighted and Double-scale YOLO), an advanced architecture for efficient plant disease detection. The PlantDoc dataset was initially enhanced using data augmentation techniques. Subsequently, we developed the DSConv module—a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. This module replaces the conventional Conv module in YOLOv10. Furthermore, the WT
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Alkhammash, Eman H. "Multi-Classification Using YOLOv11 and Hybrid YOLO11n-MobileNet Models: A Fire Classes Case Study." Fire 8, no. 1 (2025): 17. https://doi.org/10.3390/fire8010017.

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Fires are classified into five types: A, B, C, D, and F/K, according to the components involved in combustion. Recognizing fire classes is critical, since each kind demands a unique suppression approach. Proper fire classification helps to decrease the risk to both life and property. The fuel type is used to determine the fire class, so that the appropriate extinguishing agent can be selected. This study takes advantage of recent advances in deep learning, employing YOLOv11 variants (YOLO11n, YOLO11s, YOLO11m, YOLO11l, and YOLO11x) to classify fires according to their class, assisting in the s
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Çimen, Murat Erhan. "YOLOv11-based Detection of Wagon Brake Cylinder Conditions." Journal of Smart Systems Research 6, no. 1 (2025): 28–44. https://doi.org/10.58769/joinssr.1657438.

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Railway transportation stands out as a safe and efficient mode of transport for both freight and passengers. However, failures in train braking systems pose financial and safety risks. In this study, it is proposed to use the recently introduced YOLOv11 (You Only Look Once) models to monitor the mechanical brakes used in wagons. This approach aims to prevent the locking of wheels due to stuck mechanical brakes while the train is in motion, thereby avoiding continuous metal friction and mitigating risks such as Flatted wheels, wheel fractures, rail damage, and fire hazards. Such failures not on
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Zhang, Chaokai, Ningbo Peng, Jiaheng Yan, et al. "A Novel YOLOv10-DECA Model for Real-Time Detection of Concrete Cracks." Buildings 14, no. 10 (2024): 3230. http://dx.doi.org/10.3390/buildings14103230.

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The You Only Look Once (YOLO) series algorithms have been widely adopted in concrete crack detection, with attention mechanisms frequently being incorporated to enhance recognition accuracy and efficiency. However, existing research is confronted by two primary challenges: the suboptimal performance of attention mechanism modules and the lack of explanation regarding how these mechanisms influence the model’s decision-making process to improve accuracy. To address these issues, a novel Dynamic Efficient Channel Attention (DECA) module is proposed in this study, which is designed to enhance the
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Bento, João, Thuanne Paixão, and Ana Beatriz Alvarez. "Performance Evaluation of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Stamp Detection in Scanned Documents." Applied Sciences 15, no. 6 (2025): 3154. https://doi.org/10.3390/app15063154.

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Stamps are an essential mechanism for authenticating documents in various sectors and institutions. Given the high volume of documents and the increase in forgery, it is necessary to adopt automated methods to identify stamps on documents. In this context, techniques based on deep learning stand out as an efficient solution for automating this process. To this end, this article presents a performance evaluation of YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s in detecting stamps on scanned documents. To train, validate, and test the models, an adapted dataset with 732 images from the combination of
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Wang, Chongqin, Yi Guan, Minghe Chi, et al. "GPR-TSBiNet: An Information Gradient Enrichment Model for GPR B-Scan Small Target Detection." Sensors 25, no. 7 (2025): 2223. https://doi.org/10.3390/s25072223.

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Accurate detection of underground grounding lines remains a significant technical challenge due to their deep burial and small cross-sectional dimensions, which cause signal scattering in heterogeneous soil media. This results in blurred features in GPR B-scan images, impeding reliable target identification. To address this limitation, we propose GPR-TSBiNet, an architecture incorporating two key model innovations. We introduce GPR-Transformer (GPR-Trans), a multi-branch backbone network specifically designed for GPR B-scan processing. In the neck stage, we develop the Spatial-Depth Converted
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Santos Júnior, Eder Silva dos, Thuanne Paixão, and Ana Beatriz Alvarez. "Comparative Performance of YOLOv8, YOLOv9, YOLOv10, and YOLOv11 for Layout Analysis of Historical Documents Images." Applied Sciences 15, no. 6 (2025): 3164. https://doi.org/10.3390/app15063164.

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The digitalization of historical documents is of interest for many reasons, including historical preservation, accessibility, and searchability. One of the main challenges with the digitization of old newspapers involves complex layout analysis, where the content types of the document must be determined. In this context, this paper presents an evaluation of the most recent YOLO methods for the analysis of historical document layouts. Initially, a new dataset called BHN was created and made available, standing out as the first dataset of historical Brazilian newspapers for layout detection. The
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Dissertations / Theses on the topic "YOLOv10"

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Oškera, Jan. "Detekce dopravních značek a semaforů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2020. http://www.nusl.cz/ntk/nusl-432850.

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The thesis focuses on modern methods of traffic sign detection and traffic lights detection directly in traffic and with use of back analysis. The main subject is convolutional neural networks (CNN). The solution is using convolutional neural networks of YOLO type. The main goal of this thesis is to achieve the greatest possible optimization of speed and accuracy of models. Examines suitable datasets. A number of datasets are used for training and testing. These are composed of real and synthetic data sets. For training and testing, the data were preprocessed using the Yolo mark tool. The trai
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Borngrund, Carl. "Machine vision for automation of earth-moving machines : Transfer learning experiments with YOLOv3." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-75169.

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This master thesis investigates the possibility to create a machine vision solution for the automation of earth-moving machines. This research was done as without some type of vision system it will not be possible to create a fully autonomous earth moving machine that can safely be used around humans or other machines. Cameras were used as the primary sensors as they are cheap, provide high resolution and is the type of sensor that most closely mimic the human vision system. The purpose of this master thesis was to use existing real time object detectors together with transfer learning and exa
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Melcherson, Tim. "Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

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Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex region and how it is effected by climate change. Here, animals and vegetation are tightly coupled with their environment in a fragile ecosystem, and when the environment undergo rapid changes it risks damaging these ecosystems severely.  Understanding what kind of data that has potential to be used in artificial intelligence, can be of importance as many research stations have data archives from decades of work in the Arctic. In this thesis, a YOLOv4 object d
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Norling, Samuel. "Tree species classification with YOLOv3 : Classification of Silver Birch (Betula pendula) and Scots Pine (Pinus sylvestris)." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260244.

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Automation of tree species classification during a forest inventory could potentially provide more efficiency and better results for forest companies and stakeholding agencies. This thesis investigates how well a state of the art object detection system, YOLOv3, performs this classification task. A new image dataset with pictures of Silver Birches and Scots Pines, called LilljanNet, was created to train YOLOv3. After training YOLOv3 on half the dataset we performed validation by testing it against the other half. The trained model scored a mean average precision above 0.99. Training was also d
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Ståhl, Sebastian. "A tracking framework for a dynamic non- stationary environment." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288955.

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As the use of unmanned aerial vehicles (UAVs) increases in popularity across the globe, their fields of application are constantly growing. This thesis researches the possibility of using a UAV to detect, track, and geolocate a target in a dynamic nonstationary environment as the seas. In this case, different projection and apparent size of the target in the captured images can lead to ambiguous assignments of coordinated. In this thesis, a framework based on a UAV, a monocular camera, a GPS receiver, and the UAV’s inertial measurement unit (IMU) is developed to perform the task of detecting,
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Ye, Fanjie. "A Method of Combining GANs to Improve the Accuracy of Object Detection on Autonomous Vehicles." Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1752364/.

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As the technology in the field of computer vision becomes more and more mature, the autonomous vehicles have achieved rapid developments in recent years. However, the object detection and classification tasks of autonomous vehicles which are based on cameras may face problems when the vehicle is driving at a relatively high speed. One is that the camera will collect blurred photos when driving at high speed which may affect the accuracy of deep neural networks. The other is that small objects far away from the vehicle are difficult to be recognized by networks. In this paper, we present a meth
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Wang, Chen. "2D object detection and semantic segmentation in the Carla simulator." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291337.

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The subject of self-driving car technology has drawn growing interest in recent years. Many companies, such as Baidu and Tesla, have already introduced automatic driving techniques in their newest cars when driving in a specific area. However, there are still many challenges ahead toward fully autonomous driving cars. Tesla has caused several severe accidents when using autonomous driving functions, which makes the public doubt self-driving car technology. Therefore, it is necessary to use the simulator environment to help verify and perfect algorithms for the perception, planning, and decisio
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Kharel, Subash. "POTHOLE DETECTION USING DEEP LEARNING AND AREA ASSESSMENT USING IMAGE MANIPULATION." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/theses/2825.

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Every year, drivers are spending over 3 billions to repair damage on vehicle caused by potholes. Along with the financial disaster, potholes cause frustration in drivers. Also, with the emerging development of automated vehicles, road safety with automation in mind is being a necessity. Deep Learning techniques offer intelligent alternatives to reduce the loss caused by spotting pothole. The world is connected in such a way that the information can be shared in no time. Using the power of connectivity, we can communicate the information of potholes to other vehicles and also the department of
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Roohi, Masood. "end-point detection of a deformable linear object from visual data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21133/.

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In the context of industrial robotics, manipulating rigid objects have been studied quite deeply. However, Handling deformable objects is still a big challenge. Moreover, due to new techniques introduced in the object detection literature, employing visual data is getting more and more popular between researchers. This thesis studies how to exploit visual data for detecting the end-point of a deformable linear object. A deep learning model is trained to perform the task of object detection. First of all, basics of the neural networks is studied to get more familiar with the mechanism of the ob
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Svedberg, Malin. "Analys av inskannade arkiverade dokument med hjälp av objektdetektering uppbyggt på AI." Thesis, Högskolan i Gävle, Avdelningen för datavetenskap och samhällsbyggnad, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-32612.

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Runt om i världen finns det en stor mängd historiska dokument som endast finns i pappersform. Genom att digitalisera dessa dokument förenklas bland annat förvaring och spridning av dokumenten. Vid digitalisering av dokument räcker det oftast inte att enbart skanna in dokumenten och förvara dem som en bild, oftast finns det önskemål att kunna hantera informationen som dokumenten innehåller på olika vis. Det kan t.ex. vara att söka efter en viss information eller att sortera dokumenten utifrån informationen dem innehåller. Det finns olika sätt att digitalisera dokument och extrahera den informat
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Book chapters on the topic "YOLOv10"

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Bikienga, Moustapha, Manegaouindé Roland Tougma, Sanguirè Pascal Somda, and Boureima Zerbo. "Comparative Study of YOLOv8, YOLOv9 and YOLOv10 by Their Ability to Detect Mangoes." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0692-4_7.

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Saltık, Ahmet Oğuz, Alicia Allmendinger, and Anthony Stein. "Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91835-3_12.

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Abstract This paper presents a comprehensive evaluation of state-of-the-art object detection models, including YOLOv9, YOLOv10, and RT-DETR, for the task of weed detection in smart-spraying applications focusing on three classes: Sugarbeet, Monocot, and Dicot. The performance of these models is compared based on mean Average Precision (mAP) scores and inference times on different GPU and CPU devices. We consider various model variations, such as nano, small, medium, large alongside different image resolutions (320px, 480px, 640px, 800px, 960px). The results highlight the trade-offs between inf
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Qie, Longfei, Junjie He, Chunlei Chai, A. Senthil Kumar, and Ruixue Wang. "Research on Robot Grasping Based on YOLOv10-GGCNN." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4710-1_36.

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Visweswari, Tamminana, Sabbineni Keerthika, Devika Goud Pandala, M. Pooja, and B. Usha Rani. "Smart E-Waste management using YOLOv10 and RMSprop." In Multi-Disciplinary Research and Sustainable Development. CRC Press, 2025. https://doi.org/10.1201/9781003675242-11.

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Ballout, Hadi, Riccardo Berta, Ali Dabbous, et al. "Performance Comparison of YOLOv8 and YOLOv10 for Traffic Light Detection on a Jetson Nano Board." In Lecture Notes in Electrical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84100-2_29.

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Ahmed, Shakil, Monir Hossain, Amit Azim Amit, Mahdia Tahsin, Mufti Mahmud, and M. Shamim Kaiser. "Real-Time Bangla Sign Language Detection and Recognition Using YOLOv10." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-0185-1_33.

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Zhou, Zhitan, Qichen Zheng, Qiang Yang, and Yitao Ma. "Real-Time High-Precision Detection Technology for Aircraft Target in SAR Image Based on YOLOv9 and YOLOv10." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86203-8_6.

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Fang, Enquan, Shijiao Li, Zhen Liu, Yaodong Wang, and Tao Tao. "Research on Real-Time Inspection Scheme of Tunnel Lining Surface Defects Based on YOLOv10." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3969-4_3.

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Liu, Zhiyuan, Yan Li, Zhanmou Xu, et al. "Recognition Method for Train Coupler Handle Based on YOLOv5 Model." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1876-4_88.

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AbstractTo solve the problem of identifying different types of car couplers during the operation of the automatic uncoupling robot of a tippler, a method for recognizing the handle of a car coupler based on the YOLOv5 model has been proposed. This method selects YOLOv5n, which is relatively simple in the YOLOv5 series, as the benchmark model for the detection network. The overall structure is more concise, effectively reducing the number of model parameters while ensuring detection accuracy. The YOLOv5n model used for feature extraction and target recognition on two types of coupler datasets:
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Kuznetsova, Anna, Tatiana Maleva, and Vladimir Soloviev. "YOLOv5 versus YOLOv3 for Apple Detection." In Studies in Systems, Decision and Control. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66077-2_28.

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Conference papers on the topic "YOLOv10"

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Luo, Zhiping, Yilai Zhang, and Chao Li. "A Comparative Study of YOLOv8, YOLOv9, and YOLOv10 in Tile Defect Detection." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872483.

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Mulla, Sufiya, Rajlaxmi Mandavkar, Simran Jamadar, Sneha Magdum, and Uma Gurav. "Camouflaged Human Detection: Comparative Analysis Using YoloV5s, Yolov5l, Yolov5m, Yolov5n, Yolov5x Model." In 2024 4th International Conference on Artificial Intelligence and Signal Processing (AISP). IEEE, 2024. https://doi.org/10.1109/aisp61711.2024.10870666.

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Nguyen, Dat Minh-Tien, and Thien Huynh-The. "RS-YOLOv10: Enhancing YOLOv10 for Accurate Small-Object Detection." In 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2025. https://doi.org/10.1109/imcom64595.2025.10857573.

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Chomklin, Amonpan, Saichon Jaiyen, Niwan Wattanakitrungroj, Pornchai Mongkolnam, and Suluk Chaikhan. "Packaging Defect Detection in Lean Manufacturing: A Comparative Study of YOLOv8, YOLOv9, and YOLOv10." In 2024 28th International Computer Science and Engineering Conference (ICSEC). IEEE, 2024. https://doi.org/10.1109/icsec62781.2024.10770712.

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Li, Muyang, and Chunlin Song. "YOLOv10-CMR: An Improved Small Object Detection Algorithm Based on YOLOv10." In 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE). IEEE, 2025. https://doi.org/10.1109/nnice64954.2025.11064323.

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Liu, Ruiyang. "Improved LKM-YOLOv10 Vehicle Licence Plate Recognition Detection System Based on YOLOv10." In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS). IEEE, 2024. https://doi.org/10.1109/eiecs63941.2024.10800394.

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A’la, Fiddin Yusfida, Nurul Firdaus, Hartatik, and Helmi Imaduddin. "Precision in Safety: YOLOv9 vs. YOLOv10 for Helmet Image Detection." In 2024 International Visualization, Informatics and Technology Conference (IVIT). IEEE, 2024. http://dx.doi.org/10.1109/ivit62102.2024.10692595.

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ArdaÇ, Fatma Betül Kara, and Pakize Erdogmus. "Car Object Detection: Comparative Analysis of YOLOv9 and YOLOv10 Models." In 2024 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2024. https://doi.org/10.1109/asyu62119.2024.10756955.

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Xie, Xuhui, Junkai Ren, Yujun Zeng, Shanling Wei, Yuxin Wang, and Wenting Luan. "HATSC-YOLOv10:Improved YOLOv10 for Satellite Remote Sensing Images of Small Object Detection." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865623.

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Ma, Lin, Jieming Lin, and Min Liu. "YOLOv10-Turbo: a YOLOv10-based infrared target detection model for unmanned aerial vehicles." In International Conference on Advances in Computer Vision Research and Applications, edited by Zhonghong Ou and Hui Liu. SPIE, 2025. https://doi.org/10.1117/12.3068177.

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Reports on the topic "YOLOv10"

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Schoening, Timm. PyiFDOYOLO. GEOMAR, 2022. http://dx.doi.org/10.3289/sw_2_2022.

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Yoo, Shinjae, Yonggang Cui, Ji Hwan Park, Yuewei Lin, and Yihui Ren. Development of a software tool for IAEA use of the YOLOv3 machine learning algorithm. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1494041.

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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping University Electronic Press, 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were test
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