Relevant bibliographies by topics / YOLOv11

Contents

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

Academic literature on the topic 'YOLOv11'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "YOLOv11"

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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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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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Waskito, Deswal, Dian Farah Syarifah, and Rizky Ajie Aprilianto. "Comparison of the Use of YOLOv11 Variations in the Empty Parking Spaces Detection System." Sainteknol : Jurnal Sains dan Teknologi 23, no. 1 (2025): 1–10. https://doi.org/10.15294/sainteknol.v23i1.20014.

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The development of a smart parking system using the You Only Look Once (YOLO) model has improved the efficiency of parking management by providing real-time vehicle detection and availability of parking spaces. This study compared three variations of YOLOv11-Nano (YOLOv11n), YOLOv11-Small (YOLOv11s), and YOLOv11-Medium (YOLOv11m) to determine the most effective model in detecting empty parking spaces. The experiment was carried out using a dataset consisting of 5725 images of parking areas with various conditions such as angles, lighting, and distance. In addition, the researcher also used a 6
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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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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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Eva Urankar. "Waste Detection on Mobile Devices: Model Performance and Efficiency Comparison." International Journal of Science and Research Archive 15, no. 1 (2024): 722–31. https://doi.org/10.30574/ijsra.2025.15.1.1052.

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This study evaluates object detection models for mobile deployment by comparing YOLOv11 and EfficientDet-Lite using a waste classification dataset. EfficientDet-Lite0 demonstrated higher speed (13 FPS), YOLOv11n was the most power-efficient (125,000 μAh in 590 seconds), and YOLOv11m achieved the highest accuracy (mAP@50: 0.694). The deployment of these models on an Android application highlights their trade-offs: EfficientDet-Lite0 suits speed-critical tasks, YOLOv11n excels in power-sensitive scenarios, and YOLOv11m and YOLOv11s perform best in accuracy-driven applications. These findings inf
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Džakula, Nebojša Bačanin, Rudi Heriansyah, and Fadly Fadly. "Performance Evaluation of YOLOv10 and YOLOv11 on Blood Cell Object Detection Dataset." International Journal of Advances in Artificial Intelligence and Machine Learning 2, no. 2 (2025): 95–103. https://doi.org/10.58723/ijaaiml.v2i2.434.

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Background of study: Blood cell analysis is vital for diagnosing medical conditions, but traditional manual methods are laborious and error-prone. Deep learning, especially YOLO models, offers automated solutions for medical image analysis. However, the real-world effectiveness of the latest YOLOv11 in blood cell detection is not thoroughly investigated, as general object detection improvements may not translate to biomedical images due to their unique characteristics.Aims and scope of paper: This study systematically compares YOLOv10 and YOLOv11 on a public blood cell detection dataset to ass
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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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Sutikno, Aris Sugiharto, and Retno Kusumaningrum. "Enhanced Automatic License Plate Detection and Recognition using CLAHE and YOLOv11 for Seat Belt Compliance Detection." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 20271–78. https://doi.org/10.48084/etasr.9629.

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Traffic accidents caused by seat belt violations remain a severe problem in low-income countries. Identifying the vehicles of these violators is vital for enhancing safety. Therefore, this research develops a vehicle license plate detection and recognition system to support this problem. The proposed system was divided into three subsystems: windshield detection, license plate detection, and character recognition. The windshield detection subsystem used the You Only Look Once (YOLOv11) model. License plate detection combined the determination of the Region Of Interest (ROI) and YOLOv11. Meanwh
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Dissertations / Theses on the topic "YOLOv11"

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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 "YOLOv11"

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Chen, Hong, Jue Zhou, and Qingling Zhao. "YOLO-PFS: Improved YOLOv11 for Remote Sensing Object Detection and Recognition." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9815-8_38.

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Cheng, Yiyuan, Lianghao Gong, Zhuohao Ning, Kuan Li, and Jianping Yin. "Using an Improved Lightweight YOLOv11 Model for Fuzzy Image Object Detection." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9911-7_23.

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Akhtar, Sania, Muhammad Hanif, Hamdi Melih Saraoglu, Sham Lal, and Muhammad Waqas Arshad. "YOLOv11-SAMNet: A Hybrid Detection and Segmentation Framework for Urine Sediment Analysis." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97663-6_21.

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Li, Xinkang, Liejun Wang, and Shaochen Jiang. "GCDN: A Novel YOLOv11-Based Approach for Cotton Pest and Disease Detection." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9866-0_19.

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Li, Yiqing, and Ke Xu. "Infrared Multi-Scale Target Detection Based on Improved YOLOv11 and Spatiotemporal Features." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-95-0009-3_39.

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Jiang, Yan, Zhitao Dai, Yu Chen, and Zhiqiang Chu. "An Improved Multi-task Model for Instance Segmentation and Pose Estimation Based on YOLOv11." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9856-1_19.

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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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Cai, Xingquan, Luyao Wang, Junru Zhang, Lixin Ding, and Ying Li. "HDF-YOLO: A High-Precision Ship Detection Method in SAR Images Based on Improved YOLOv11." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9794-6_19.

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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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Nguyen, Thi Diem Huong, Van Loc An Ho, and Vinh Dinh Nguyen. "Multimodal Approach for Canine Dermatological and Ophthalmological Disease Diagnosis Using YOLOv11 with Data Augmentation and Autoencoder Techniques." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97000-9_23.

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Conference papers on the topic "YOLOv11"

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M, Lokesh Kumar K., and N. Aishwarya. "Underwater Fish Detection Using YOLOv11 and YOLOv12." In 2025 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2025. https://doi.org/10.1109/icssas66150.2025.11081079.

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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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Song, Qiong, Siwei Liu, Kaiheng Dai, and Kun Bai. "YOLOv11-DEC: An Improved YOLOv11 Model for UAV Detection in Complex Contexts." In 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2025. https://doi.org/10.1109/cscwd64889.2025.11033339.

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Liu, Yinyin, Yueting Gao, Chenxin Li, Guohui Liu, Quanguo Lu, and Fangzhi Gui. "YOLOv11-AUC: Improved YOLOv11 for Coal Fly Ash Silo Weld Surface Defect." In 2025 2nd International Conference on Digital Image Processing and Computer Applications (DIPCA). IEEE, 2025. https://doi.org/10.1109/dipca65051.2025.11042449.

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Syamsul, Marshaniswah, and Suryo Adhi Wibowo. "Optimizers Comparative Analysis on YOLOv8 and YOLOv11 for Small Object Detection." In 2024 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA). IEEE, 2024. https://doi.org/10.1109/icicyta64807.2024.10912942.

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Shaik, Abdul Basheer, Ajay Kumar Kandula, Gnana Kartheek Tirumalasetti, Baladithya Yendluri, and Hemantha Kumar Kalluri. "Comparative Analysis of YOLOv11 and YOLOv12 for Automated Weed Detection in Precision Agriculture." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11036078.

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R, Nancy Lydia, Raja Subramanian R, Gayathri Devi M, Santhosh Nantha A, and Suresh Kumar N. "Advancing Automated Jasmine Flower Detection: A comparative study of YOLOv8 and YOLOv11." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11052909.

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Narvekar, Ashish, Shreedatta Sawant, Tejas Ratikant Parab, Sehal M. Chodankar, Sahil Halankar, and Sidhanth Mandrekar. "Evaluating YOLOv8 and YOLOv11 for Real-Time Person Detection in Crowded Environments." In 2025 Seventh International Conference on Computational Intelligence and Communication Technologies (CCICT). IEEE, 2025. https://doi.org/10.1109/ccict65753.2025.00078.

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Zhou, Ziyi. "Traffic accident detection based on YOLOv11." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898397.

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Elnady, Norhan, Aya Adel, and Wael Badawy. "Using YOLOv11 for Dental Caries Detection." In 2024 12th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC). IEEE, 2024. https://doi.org/10.1109/jac-ecc64419.2024.11061242.

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Reports on the topic "YOLOv11"

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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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