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Journal articles on the topic 'YOLOv10'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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Hendriko, Viky, and Dedy Hermanto. "Performance Comparison of YOLOv10, YOLOv11, and YOLOv12 Models on Human Detection Datasets." Brilliance: Research of Artificial Intelligence 5, no. 1 (2025): 440–50. https://doi.org/10.47709/brilliance.v5i1.6447.

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One popular of object detection model for object detection is You Only Look Once (YOLO) with humans are among the most often utilized for detection objects. Despite the various of human datasets, just a few research that compared the datasets performance against various versions of the YOLO algorithm. This study compares the performance of YOLOv10, YOLOv11, and YOLOv12 on eight different datasets, such as CrowdHuman, CityPersons, Wider Person, Mall Dataset, INRIA, Microsoft Common Object (MS COCO), PASCAL VOC, and MOT17. Precision, recall, mAP@50, and mAP@50-95 are used to measure the YOLO mod

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Huang, Nan-Chieh, Arvind Mukundan, Riya Karmakar, Syna Syna, Wen-Yen Chang, and Hsiang-Chen Wang. "Novel Snapshot-Based Hyperspectral Conversion for Dermatological Lesion Detection via YOLO Object Detection Models." Bioengineering 12, no. 7 (2025): 714. https://doi.org/10.3390/bioengineering12070714.

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Objective: Skin lesions, including dermatofibroma, lichenoid lesions, and acrochordons, are increasingly prevalent worldwide and often require timely identification for effective clinical management. However, conventional RGB-based imaging can overlook subtle vascular characteristics, potentially delaying diagnosis. Methods: A novel spectrum-aided vision enhancer (SAVE) that transforms standard RGB images into simulated narrowband imaging representations in a single step was proposed. The performances of five cutting-edge object detectors, based on You Look Only Once (YOLOv11, YOLOv10, YOLOv9,

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Hyun-Ki Jung. "YOLO-Drone: An Efficient Object Detection Approach Using the GhostHead Network for Drone Images." Journal of Information Systems Engineering and Management 10, no. 26s (2025): 236–47. https://doi.org/10.52783/jisem.v10i26s.4216.

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Object detection using images or videos captured by drones is a promising technology with significant potential across various industries. However, a major challenge is that drone images are typically taken from high altitudes, making object identification difficult. This paper proposes an effective solution to address this issue. The base model used in the experiments is YOLOv11, the latest object detection model, with a specific implementation based on YOLOv11n. The experimental data were sourced from the widely used and reliable VisDrone dataset, a standard benchmark in drone-based object d

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Zhang, Jianing. "Evolution of YOLO: A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10." Applied and Computational Engineering 146, no. 1 (2025): 15–23. https://doi.org/10.54254/2755-2721/2025.21591.

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This paper presents a systematic comparative analysis of three versions of the YOLO (You Only Look Once) target detection algorithm - YOLOv5, YOLOv8 and YOLOv10. Through experiments on the VOC2012 dataset (converted to COCO format), this paper evaluates the versions in terms of multiple dimensions such as detection performance, inference speed and model complexity. The experimental results show that the detection accuracy and robustness significantly improve with version iteration and the mAP of v8 v10 is improved by 6.69% and 9.12% relative to v5, However, the number of model parameters incre

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Kamolova, Shirinoy, Haydeer Mohamad Abbas, Dadaxon Abdullayev, Chandrasekharan G, Inomjon Matkarimov, and Lalit Sachdeva. "Automated disease identification in aquaculture utilizing underwater imaging and YOLOV10 network." International Journal of Aquatic Research and Environmental Studies 5, S1 (2025): 87–94. https://doi.org/10.70102/ijares/v5s1/5-s1-10.

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In intense fish farming, continuous identification and surveillance of prevalent infectious diseases are crucial for formulating scientific methods for fish disease avoidance, which may significantly mitigate the death of fish and financial damage. Nonetheless, subpar underwater imagery and poorly identifiable targets pose significant obstacles to detecting infected fish. This research proposes an Automated Disease Identification (ADI) system using Underwater Imaging (UI) and an Improved YOLOV10 Network to address these problems. This work introduces an innovative residual awareness unit refer

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Geng, Ranran, Haibin Wang, Haoyan Hu, and Teng Shi. "AFD-YOLOv10: A Lightweight Method for Non-Destructive Testing of Fusion Weld Seam Defects." Symmetry 17, no. 6 (2025): 886. https://doi.org/10.3390/sym17060886.

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In industrial inspection, X-ray detection methods are the mainstream approach for non-destructive testing (NDT) of weld defects. In response to the issues of insufficient detection accuracy and slow detection speed in existing X-ray weld defect detection (WDD) methods, a lightweight X-ray WDD model, AFD-YOLOv10, based on an improved YOLOv10n, is proposed. First, by introducing variable kernel convolution (AKConv) to replace traditional convolution in the backbone network, the model better adapts to the multi-scale variations in weld defects while maintaining its lightweight nature. Second, a l

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Baruah, Priyankush Kaushik, and Dr Pranabjyoti Haloi. "Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies." International Journal of Innovative Technology and Exploring Engineering 14, no. 6 (2025): 20–26. https://doi.org/10.35940/ijitee.e1083.14060525.

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This study presents an automated license plate detection and recognition system, combining YOLOv10 for Realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97 Parsant detection accuracy and real-time performanc

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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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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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Cheng, Haodong, and Fei Kang. "Corrosion Detection and Grading Method for Hydraulic Metal Structures Based on an Improved YOLOv10 Sequential Architecture." Applied Sciences 14, no. 24 (2024): 12009. https://doi.org/10.3390/app142412009.

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Herein, we present a method for detecting and determining the corrosion level of hydraulic metal structure surfaces through images while reducing the difficulty of dataset annotation. To achieve accurate detection of corrosion targets, the MobileViTv3 block is integrated into YOLOv10, resulting in the proposed YOLOv10-vit for corrosion target detection. Based on YOLOv10-vit, the YOLOv10-vit-cls classification network is introduced for corrosion level determination. This network leverages the pre-trained parameters of YOLOv10-vit to more quickly learn the features of different corrosion levels.

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Wang, Hongli, Qiangwen Zong, Yang Liao, et al. "Lightweight Helmet-Wearing Detection Algorithm Based on StarNet-YOLOv10." Processes 13, no. 4 (2025): 946. https://doi.org/10.3390/pr13040946.

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The safety helmet is the equipment that construction workers must wear, and it plays an important role in protecting their lives. However, there are still many construction workers who do not pay attention to the wearing of helmets. Therefore, the real-time high-precision intelligent detection of construction workers’ helmet wearing is crucial. To this end, this paper proposes a lightweight helmet-wearing detection algorithm based on StarNet-YOLOv10. Firstly, the StarNet network structure is used to replace the backbone network part of the original YOLOv10 model while retaining the original Sp

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Dewi, Christine, Rung-Ching Chen, Yong-Cun Zhuang, and Henoch Juli Christanto. "Yolov5 Series Algorithm for Road Marking Sign Identification." Big Data and Cognitive Computing 6, no. 4 (2022): 149. http://dx.doi.org/10.3390/bdcc6040149.

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Road markings and signs provide vehicles and pedestrians with essential information that assists them to follow the traffic regulations. Road surface markings include pedestrian crossings, directional arrows, zebra crossings, speed limit signs, other similar signs and text, and so on, which are usually painted directly onto the road surface. Road markings fulfill a variety of important functions, such as alerting drivers to the potentially hazardous road section, directing traffic, prohibiting certain actions, and slowing down. This research paper provides a summary of the Yolov5 algorithm ser

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Mei, Junhui, and Wenqiu Zhu. "BGF-YOLOv10: Small Object Detection Algorithm from Unmanned Aerial Vehicle Perspective Based on Improved YOLOv10." Sensors 24, no. 21 (2024): 6911. http://dx.doi.org/10.3390/s24216911.

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With the rapid development of deep learning, unmanned aerial vehicles (UAVs) have acquired intelligent perception capabilities, demonstrating efficient data collection across various fields. In UAV perspective scenarios, captured images often contain small and unevenly distributed objects, and are typically high-resolution. This makes object detection in UAV imagery more challenging compared to conventional detection tasks. To address this issue, we propose a lightweight object detection algorithm, BGF-YOLOv10, specifically designed for small object detection, based on an improved version of Y

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Silvanus, Tumanan. "Comparative Analysis of YOLOv8, YOLOv9, and YOLOv10 for Object Detection: Performance Metrics and Real-World Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 5045–51. https://doi.org/10.22214/ijraset.2025.71284.

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Abstract: In computer vision, object detection is still an essential job with applications in robotics, autonomous cars, and surveillance. The YOLO (You Only Look Once) model family is a well-liked option for real-time object recognition because of its reputation for striking a balance between speed and accuracy. Using the Pascal VOC 2012 dataset, this study presents a comparative examination of three YOLO variants: YOLOv8, YOLOv9, and YOLOv10. Key metrics included in the research are F1-score, accuracy, recall, and mean Average accuracy (mAP). Furthermore, a loss curve analysis is performed t

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Mohammed Abdul Jaleel Maktoof, Israa Tahseen Ali Al_attar, and Ibraheem Nadher Ibraheem. "Comparison YOLOv5 Family for Human Crowd Detection." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 04 (2023): 94–108. http://dx.doi.org/10.3991/ijoe.v19i04.39095.

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Recent years have seen widespread application of crowd counting and detection technology in areas as varied as urban preventing crime, station crowd statistics, and people flow studies. However, getting accurate placements and improving audience counting performance in dense scenes still has challenges, and it pays to devote a lot of effort to it. In this paper, crowd counting models are proposed based on the YOLOv5 algorithm, and four YOLOv5 models (YOLOv5l, YOLOv5m, YOLOv5s, YOLOv5x) were built for the purpose of comparing the models and increasing the accuracy of crowd identification as eac

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Wu, Qiong, Hang Liu, Hongfei Zhu, et al. "YOLO_SSP: An Auto-Algorithm to Detect Mature Soybean Stem Nodes Based on Keypoint Detection." Agronomy 15, no. 5 (2025): 1128. https://doi.org/10.3390/agronomy15051128.

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The soybean stem node is a key part of soybean growth and development, and its numbers play a crucial role in soybean yield formation. Traditional manual methods are labor-intensive and error-prone. The keypoint detection method is an ideal choice for stem node detection due to its high accuracy and wide applicability. In this study, a new deep learning method, You Only Look Once _Soybean Stalk Pose (YOLO_SSP) was proposed, which innovatively applied the Small_Effective Low-Level Aggregation Network (S_ELAN) module and fused it with a smaller detection head for detecting stem nodes in mature s

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Guarnido-Lopez, Pablo, John-Fredy Ramirez-Agudelo, Emmanuel Denimal, and Mohammed Benaouda. "Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m." Animals 14, no. 19 (2024): 2821. http://dx.doi.org/10.3390/ani14192821.

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This study highlights the importance of monitoring cattle feeding behavior using the YOLO algorithm for object detection. Videos of six Charolais bulls were recorded on a French farm, and three feeding behaviors (biting, chewing, visiting) were identified and labeled using Roboflow. YOLOv8 and YOLOv10 were compared for their performance in detecting these behaviors. YOLOv10 outperformed YOLOv8 with slightly higher precision, recall, mAP50, and mAP50-95 scores. Although both algorithms demonstrated similar overall accuracy (around 90%), YOLOv8 reached optimal training faster and exhibited less

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Jiang, Bo, Jian-Lin Zhang, Wen-Hao Su, and Rui Hu. "A SPH-YOLOv5x-Based Automatic System for Intra-Row Weed Control in Lettuce." Agronomy 13, no. 12 (2023): 2915. http://dx.doi.org/10.3390/agronomy13122915.

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Weeds have a serious impact on lettuce cultivation. Weeding is an efficient way to increase lettuce yields. Due to the increasing costs of labor and the harm of herbicides to the environment, there is an increasing need to develop a mechanical weeding robot to remove weeds. Accurate weed recognition and crop localization are prerequisites for automatic weeding in precision agriculture. In this study, an intra-row weeding system is developed based on a vision system and open/close weeding knives. This vision system combines the improved you only look once v5 (YOLOv5) identification model and th

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Liao, Liefa, Chao Song, Shouluan Wu, and Jianglong Fu. "A Novel YOLOv10-Based Algorithm for Accurate Steel Surface Defect Detection." Sensors 25, no. 3 (2025): 769. https://doi.org/10.3390/s25030769.

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To address challenges like manual processes, complicated detection methods, high false alarm rates, and frequent errors in identifying defects on steel surfaces, this research presents an innovative detection system, YOLOv10n-SFDC. The study focuses on the complex dependencies between parameters used for defect detection, particularly the interplay between feature extraction, fusion, and bounding box regression, which often leads to inefficiencies in traditional methods. YOLOv10n-SFDC incorporates advanced elements such as the DualConv module, SlimFusionCSP module, and Shape-IoU loss function,

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Yuan, Wenxia, Chunhua Yang, Xinghua Wang, et al. "CV-YOLOv10-AR-M: Foreign Object Detection in Pu-Erh Tea Based on Five-Fold Cross-Validation." Foods 14, no. 10 (2025): 1680. https://doi.org/10.3390/foods14101680.

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To address the problem of detecting foreign bodies in Pu-erh tea, this study proposes an intelligent detection method based on an improved YOLOv10 network. By introducing the MPDIoU loss function, the YOLOv10 network is optimized to effectively enhance the positioning accuracy of the model in complex background and improve detection of small target foreign objects. Using AssemFormer to optimize the structure, the network’s ability to perceive small target foreign objects and its ability to process global information are improved. By introducing the Rectangular Self-Calibrated Module, the predi

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Arifando, Rio, Shinji Eto, Tibyani Tibyani, and Chikamune Wada. "Improved YOLOv10 for Visually Impaired: Balancing Model Accuracy and Efficiency in the Case of Public Transportation." Informatics 12, no. 1 (2025): 7. https://doi.org/10.3390/informatics12010007.

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Advancements in automation and artificial intelligence have significantly impacted accessibility for individuals with visual impairments, particularly in the realm of bus public transportation. Effective bus detection and bus point-of-view (POV) classification are crucial for enhancing the independence of visually impaired individuals. This study introduces the Improved-YOLOv10, a novel model designed to tackle challenges in bus identification and pov classification by integrating Coordinate Attention (CA) and Adaptive Kernel Convolution (AKConv) into the YOLOv10 framework. The Improved YOLOv1

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Wang, Quanwei, Xiaoyang Wang, Jiayi Hou, Xuying Liu, Hao Wen, and Ziya Ji. "MF-YOLOv10: Research on the Improved YOLOv10 Intelligent Identification Algorithm for Goods." Sensors 25, no. 10 (2025): 2975. https://doi.org/10.3390/s25102975.

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To enhance the accuracy of identifying parts and goods in automated loading and unloading machines, this study proposes a lightweight detection model, MF-YOLOv10, based on intelligent recognition of goods’ shape, color, position, and environmental interference. The algorithm significantly improves the feature extraction and detection capabilities by replacing the traditional IoU loss function with the MPDIoU and introducing the SCSA attention module. These enhancements improve the detection performance of multi-scale targets, enabling the improved YOLOv10 model to achieve precise recognition o

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Zhang, Quanyu, Xin Wang, Heng Shi, et al. "BRA-YOLOv10: UAV Small Target Detection Based on YOLOv10." Drones 9, no. 3 (2025): 159. https://doi.org/10.3390/drones9030159.

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Unmanned aerial vehicle (UAV) targets are typically small in size, occupy only a limited pixel area, and are often located in complex environments. Existing models, however, tend to overlook smaller targets in complex backgrounds, making it easy to miss important information and resulting in missing targets. This paper proposes an innovative UAV detection method called BRA-YOLOv10. Firstly, Bi-Level Routing Attention (BRA) is used during the feature extraction stage to effectively reduce background interference. By focusing on the target’s key information, the model optimizes overall detection

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Hu, Erzhizhi. "YOLOv10-based Model for Player and Football Detection." Journal of Computing and Electronic Information Management 15, no. 2 (2024): 30–35. https://doi.org/10.54097/2sx59328.

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This study presents an advanced YOLOv10n-based method for the automatic detection of football players and balls directly from match videos. We enhance the YOLOv10 architecture with several significant improvements, including additional detection heads, the integration of C2f_faster and C3_faster modules for enhanced processing speed and accuracy, and the inclusion of BotNet modules with self-attention mechanisms for managing complex visual scenes. Further, we incorporate GhostConv modules to reduce computational overhead while maintaining effective feature extraction. These architectural modif

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Macherla, Prasanna Kumar, Anitha Telagareddi, Nirmal Kollipara, and Hima Bindu Bommareddy. "Real Time Moving Object Detection Using YOLO." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1799–802. https://doi.org/10.22214/ijraset.2025.67304.

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Abstract: The YOLOv10 explores a cutting-edge advancement in real-time object detection, widely used in robotics, autonomous vehicles, and surveillance for its enhanced speed and accuracy. YOLOv10 builds on earlier versions by integrating improved convolutional layers, anchor boxes, and transformer-based modules, enabling more efficient object identification in a single neural network run, ideal for time- sensitive applications. The research examines advanced training techniques such as refined data augmentation, optimization, and novel loss functions, with tests on datasets like COCO and PASC

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Sazak, Halenur, and Muhammed Kotan. "Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights." Diagnostics 15, no. 1 (2024): 22. https://doi.org/10.3390/diagnostics15010022.

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Background/Objectives: Accurate detection and classification of blood cell types in microscopic images are crucial for diagnosing various hematological conditions. This study aims to develop and evaluate advanced architectures for automating blood cell detection and classification using the newly proposed YOLOv10 and YOLOv11 models, with a specific focus on identifying red blood cells (RBCs), white blood cells (WBCs), and platelets in microscopic images as a preliminary step of the complete blood count (CBC). Methods: The Blood Cell Count Detection (BCCD) dataset was enriched using data augmen

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Ruengrote, Sumarin, Kittikun Kasetravetin, Phanuphop Srisom, Theeraphan Sukchok, and Don Kaewdook. "Design of Deep Learning Techniques for PCBs Defect Detecting System based on YOLOv10." Engineering, Technology & Applied Science Research 14, no. 6 (2024): 18741–49. https://doi.org/10.48084/etasr.9028.

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As Printed Circuit Boards (PCBs) are critical components in electronic products, their quality inspection is crucial. This study focuses on quality inspection to detect PCB defects using deep learning techniques. Traditional widely used quality control methods are time-consuming, labor-intensive, and prone to human errors, making the manufacturing process inefficient. This study proposes a deep-learning approach using YOLOv10. Through the incorporation of architectural improvements such as CSPNet and PANet that improve feature extraction and fusion, as well as a dual assignments mechanism that

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Rosalina, Rosalina, Fitri Bimantoro, and I. Gede Pasek Suta Wijaya. "STUDENT FOCUS DETECTION USING YOU ONLY LOOK ONCE V5 (YOLOV5) ALGORITHM." Jurnal Teknik Informatika (Jutif) 5, no. 5 (2024): 1203–11. https://doi.org/10.52436/1.jutif.2024.5.5.1977.

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Education has a very important role in life, student involvement in the learning process in the classroom is an important factor in the success of learning. However, some students pay less attention to the lesson, indicating a lack of productivity in learning. The use of machine learning and computer vision techniques has undergone significant development in the last decade and is applied in a variety of applications, including monitoring student attention in the classroom. One of the commonly used techniques in machine learning and computer vision to detect objects is by applying image proces

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Li, Ao, Chunrui Wang, Tongtong Ji, Qiyang Wang, and Tianxue Zhang. "D3-YOLOv10: Improved YOLOv10-BasedLightweight Tomato Detection Algorithm Under Facility Scenario." Agriculture 14, no. 12 (2024): 2268. https://doi.org/10.3390/agriculture14122268.

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Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and different fruit sizes, low accuracy on edge location, and heavy model parameters. To address these problems, this paper proposed D3-YOLOv10, a lightweight YOLOv10-based detection framework. Initially, a compact dynamic faster network (DyFasterNet) was developed, where multiple adaptive con

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Gusti Audryadmaja, Anugerah Ekha. "Implementasi ROS dan Optimasi Identifikasi Warna Buoy Dengan YOLOv5 Pada Miniatur Autonomous Surface Vehicle." Jurnal Elektronika dan Otomasi Industri 11, no. 1 (2024): 299–308. http://dx.doi.org/10.33795/elkolind.v11i1.5343.

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Autonomous Surface Vehicles (ASV) telah menjadi fokus penelitian yang populer karena aplikasinya yang luas. Tantangan utama dalam pengembangan ASV adalah mendeteksi dan mengidentifikasi objek di permukaan udara, seperti pelampung , dengan cepat dan akurat. Penelitian ini mengintegrasikan Robot Operating System (ROS ) dengan algoritma YOLOv5 untuk mendeteksi pelampung berwarna, dengan tujuan mengidentifikasi varian YOLOv5 yang menawarkan kinerja komputasi ringan dan akurasi yang tinggi untuk aplikasi real-time pada ASV. Berbagai varian YOLOv5 (YOLOv5s, YOLOv5m, dan YOLOv5L) dievaluasi berdasark

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Leong, Wai Yie, and Yan Li. "YOLOv10 Algorithm for Real-Time Pedestrian Detection in Autonomous Vehicles." ASM Science Journal 20, no. 1 (2025): 1–7. https://doi.org/10.32802/asmscj.2025.1977.

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Autonomous driving safety requires accurate pedestrian detection. This study introduces real-time pedestrian detection based on the YOLOv10 algorithm. Adding EfficientNet, C2F-DM, BiFormer, and a multi-scale feature fusion detection head to the backbone, neck, and multi-scale networks creates a real-time object detection model. Experiments demonstrate that YOLOv10 can detect multi-scale pedestrians in complicated settings. The implementation of YOLOv10 for pedestrian detection in Autonomous Vehicles advances the field of intelligent transportation systems and contributes to the broader goal of

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Ulandari, Alisyia Kornelia, Fitri Bimantoro, and I. Gede Pasek Suta Wijaya. "Real Time Student Emotion Detection using Yolov5." Edumatic: Jurnal Pendidikan Informatika 8, no. 1 (2024): 222–31. http://dx.doi.org/10.29408/edumatic.v8i1.25726.

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The introduction of technology in the field of Education, especially in learner emotion detection plays an important role in the modern educational context. This research introduces the application of the YOLOV5 algorithm to detect learner emotions in real time during the classroom learning process. This research aims to see the performance of YOLOv5 in detecting student emotions by comparing YOLOv5 variants, namely YOLOv5m, YOLOv5n, YOLOv5l, YOLov5s, and YOLOv5x. The dataset used is a video recording of the learning process taken in classroom A3-02 in Building A, Informatics Engineering Study

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Mao, Makara, Ahyoung Lee, and Min Hong. "Efficient Fabric Classification and Object Detection Using YOLOv10." Electronics 13, no. 19 (2024): 3840. http://dx.doi.org/10.3390/electronics13193840.

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The YOLO (You Only Look Once) series is renowned for its real-time object detection capabilities in images and videos. It is highly relevant in industries like textiles, where speed and accuracy are critical. In the textile industry, accurate fabric type detection and classification are essential for improving quality control, optimizing inventory management, and enhancing customer satisfaction. This paper proposes a new approach using the YOLOv10 model, which offers enhanced detection accuracy, processing speed, and detection on the torn path of each type of fabric. We developed and utilized

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Jishan, Md Asifuzzaman, Ananna Islam Bedushe, Md Ataullah Khan Rifat, Bijan Paul, and Khan Raqib Mahmud. "YOLOv3 and YOLOv5-based automated facial mask detection and recognition systems to prevent COVID-19 outbreaks." Science in Information Technology Letters 4, no. 1 (2023): 22–39. http://dx.doi.org/10.31763/sitech.v4i1.1199.

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Object detection system in light of deep learning have been monstrously effective in complex item identification task images and have shown likely in an extensive variety of genuine applications counting the Coronavirus pandemic. Ensuring and enforcing the proper use of face masks is one of the main obstacles in containing and reducing the spread of the infection among the population. This paper aims to find out how the urban population of a megacity uses facial masks correctly. Using YOLOv3 and YOLOv5, we trained and validated a brand-new dataset to identify images as "with mask", "without ma

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Tselishcheva, M. O., O. V. Petrenko, B. R. Vodianyk, and M. O. Petrenko. "The role of artificial intelligence and machine learning technology in the diagnosis of age-related macular degeneration." Archive of Ophthalmology and Maxillofacial Surgery of Ukraine 1, no. 2 (2024): 87–89. https://doi.org/10.22141/aomfs.1.2.2024.16.

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Background. According to the Beaver Dam Eye Study, the prevalence of advanced age-related macular degeneration (AMD) reaches 1.6 % among patients aged 43 to 86 years. In this context, automating the diagnostic process through the use of artificial intelligence (AI) and machine learning is a promising avenue. This study explores the potential of AI models for analyzing fundus images to detect drusen in AMD. The purpose was to investigate the feasibility and effectiveness of using artificial intelligence, specifically the YOLOv10 model, to analyze fundus camera images for AMD diagnosis. Material

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Gao, Davidson. "Automatic target detection in vehicle images based on YOLOv10 deep learning algorithm." Applied and Computational Engineering 88, no. 1 (2024): 171–78. http://dx.doi.org/10.54254/2755-2721/88/20241676.

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In this paper, we explore the effect of its application in vehicle image detection based on the latest YOLOv10 model.YOLOv10, as the latest version in the YOLO series, inherits and optimises the advantages of the previous generations of models, aiming to provide higher detection accuracy and faster inference speed. In our experiments, we firstly divided the dataset reasonably and trained the YOLOv10 model with data from the training set. By analysing the Precision-Recall curve, we found that the area below the PR curve is larger, which indicates that the AUC value of the model is close to 1, t

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Yang, Chunhua, Wenxia Yuan, Qiang Zhao, et al. "Identification of fresh leaves of Anji White Tea: S-YOLOv10-ASI algorithm fusing asymptotic feature pyra-mid network." PLOS One 20, no. 7 (2025): e0325527. https://doi.org/10.1371/journal.pone.0325527.

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This study proposes the S-YOLOv10-ASI algorithm to improve the accuracy of tea identification and harvesting by robots, integrating a slice-assisted super-reasoning technique. The algorithm improves the partial structure of the YOLOv10 network through space-to-depth convolution. The Progressive Feature Pyramid Network minimizes information loss during multi-stage transmission, enhances the saliency of key layers, resolves conflicts between objects, and improves the fusion of non-adjacent layers. Intersection over Union (IoU) is used to optimize the loss function calculation. The slice-assisted

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Priyankush, Kaushik Baruah. "Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 14, no. 6 (2025): 20–26. https://doi.org/10.35940/ijitee.E1083.14060525.

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<strong>Abstract: </strong>This study presents an automated license plate detection and recognition system, combining YOLOv10 for realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97% detection accuracy and

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Priyankush, Kaushik Baruah. "Development and Implementation of a Custom License Plate Detection and Recognition System Using YOLOv10 and Tesseract OCR: A Comprehensive Study in Computer Vision and Optical Character Recognition Technologies." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 14, no. 6 (2025): 20–26. https://doi.org/10.35940/ijitee.E1083.14060525/.

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<strong>Abstract: </strong>This study presents an automated license plate detection and recognition system, combining YOLOv10 for realtime object detection and Tesseract OCR for robust text extraction. The methodology involves training a customised YOLOv10 model on annotated vehicle datasets to localize license plates, followed by region-of-interest (ROI) filtering to enhance accuracy. Detected plates are processed with Tesseract OCR to convert visual data into machine-readable text. Evaluated using precision, recall, and inference speed metrics, the system achieves 97% detection accuracy and

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Chen, Feiyu, Yingqian Zhang, Lei Fu, Rongru Hua, Qian Zhang, and Shihao Bi. "A Comparative Review of the Next-Generation YOLO Models: YOLOv10 and YOLO11." Journal of Computer Science and Artificial Intelligence 3, no. 2 (2025): 1–6. https://doi.org/10.54097/22zsmc10.

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In recent years, the YOLO (You Only Look Once) series has remained a mainstream framework in object detection, continuously driving the balance between lightweight design and high accuracy. This paper focuses on two pivotal versions—YOLOv10 and YOLO11—and provides a systematic comparison and analysis in terms of architectural design, core modules, performance characteristics, and application scenarios. YOLOv10 introduces a unified end-to-end architecture that eliminates anchors and post-processing steps, thereby simplifying the detection pipeline and significantly improving deployment efficien

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