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Artykuły w czasopismach na temat „You only look at coefficients”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Hesham, Mai, Ghada Kareem, and Marwa Hadhoud. "Enhanced real-time glaucoma diagnosis: dual deep learning approach." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 1846–57. https://doi.org/10.11591/eei.v14i3.8495.

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Effective management of glaucoma is essential for preventing irreversible vision loss. This study introduces a novel deep learning-based network designed to enhance performance while minimizing computational complexity. The system comprises two models: the first is a hybrid model combining a customized U-Net architecture integrated with you only look at coefficients (YOLACT) is utilized to achieve accurate segmentation of the optic disc (OD) and optic cup (OC), providing detailed diagnostic insights for ophthalmologists. The second model employs you only look once version 5 (YOLOv5) for real-t

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Deng, Lu, An Zhang, Jingjing Guo, and Yingkai Liu. "An Integrated Method for Road Crack Segmentation and Surface Feature Quantification under Complex Backgrounds." Remote Sensing 15, no. 6 (2023): 1530. http://dx.doi.org/10.3390/rs15061530.

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In the present study, an integrated framework for automatic detection, segmentation, and measurement of road surface cracks is proposed. First, road images are captured, and crack regions are detected based on the fifth version of the You Only Look Once (YOLOv5) algorithm; then, a modified Residual Unity Networking (Res-UNet) algorithm is proposed for accurate segmentation at the pixel level within the crack regions; finally, a novel crack surface feature quantification algorithm is developed to determine the pixels of crack in width and length, respectively. In addition, a road crack dataset

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Chen, Wei-Yu, Yu-Reng Tsao, Jin-Yi Lai, Ching-Jung Hung, Yu-Cheng Liu, and Cheng-Yang Liu. "Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach." Measurement Science Review 22, no. 3 (2022): 107–11. http://dx.doi.org/10.2478/msr-2022-0014.

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Abstract In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The p

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Lin, Guo-Shiang, Kuan-Ting Lai, Jian-Ming Syu, Jen-Yung Lin, and Sin-Kuo Chai. "Instance Segmentation Based on Deep Convolutional Neural Networks and Transfer Learning for Unconstrained Psoriasis Skin Images." Applied Sciences 11, no. 7 (2021): 3155. http://dx.doi.org/10.3390/app11073155.

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In this paper, an efficient instance segmentation scheme based on deep convolutional neural networks is proposed to deal with unconstrained psoriasis images for computer-aided diagnosis. To achieve instance segmentation, the You Only Look At CoefficienTs (YOLACT) network composed of backbone, feature pyramid network (FPN), Protonet, and prediction head is used to deal with psoriasis images. The backbone network is used to extract feature maps from an image, and FPN is designed to generate multiscale feature maps for effectively classifying and localizing objects with multiple sizes. The predic

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Panero Martinez, Ruben, Ionut Schiopu, Bruno Cornelis, and Adrian Munteanu. "Real-Time Instance Segmentation of Traffic Videos for Embedded Devices." Sensors 21, no. 1 (2021): 275. http://dx.doi.org/10.3390/s21010275.

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The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade

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He, Lang, Shiyun Li, Junting Qiu, and Chenhaomin Zhang. "DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow." Sensors 24, no. 18 (2024): 5929. http://dx.doi.org/10.3390/s24185929.

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Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. To address this challenge, this paper introduces DIO-SLAM: Dynamic Instance Optical Flow SLAM, a VSLAM system specifically designed for dynamic environments. Initially, the detection thread employs YOLACT (You Only Look At CoefficienTs) to distinguish between rigid and non-rigid objects within the scene. Subsequently, the optic

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Wang, Kun, Zheng Zhang, Xiuzhi Yang, Di Wang, Liyi Zhu, and Shuai Yuan. "Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNs." Remote Sensing 16, no. 17 (2024): 3264. http://dx.doi.org/10.3390/rs16173264.

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Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate tha

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Chen, Yanmin, Xiu Li, Mei Jia, Jiuliang Li, Tianyang Hu, and Jun Luo. "Instance Segmentation and Number Counting of Grape Berry Images Based on Deep Learning." Applied Sciences 13, no. 11 (2023): 6751. http://dx.doi.org/10.3390/app13116751.

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In order to achieve accurate segmentation of each grape image per berry, we construct a dataset composed of red globe grape samples and use a two-stage “localization–segmentation” framework-based mask region convolutional neural network (Mask R-CNN) and one-stage “pixel classification without localization” framework-based You Only Look At CoefficienTs (YOLACT) and segmenting objects by locations (SOLO) models in the grape segmentation experiments. The optimal performance of the model Mask R-CNN was applied for further study. To address the problem of overlapping and occlusion causing inaccurat

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Shi Heng Siow, Abu Ubaidah Shamsudin, Zubair Adil Soomro, et al. "Instance Segmentation Evaluation For Traffic Signs." Journal of Advanced Research in Applied Sciences and Engineering Technology 34, no. 2 (2023): 327–41. http://dx.doi.org/10.37934/araset.34.2.327341.

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This research paper focuses on developing a traffic sign recognition system based on the You Only Look At Coefficients (YOLACT) model, a one-stage instance segmentation model that offers high performance in terms of accuracy and reliability. However, the performance of YOLACT is influenced by various conditions such as day/night and different angles of objects. Therefore, this study aims to evaluate the impact of different angles and environments on the performance of the system. The paper discusses the framework, backbone structure, prototype generation branch, mask coefficient, and mask asse

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Osorio, Kavir, Andrés Puerto, Cesar Pedraza, David Jamaica, and Leonardo Rodríguez. "A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images." AgriEngineering 2, no. 3 (2020): 471–88. http://dx.doi.org/10.3390/agriengineering2030032.

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Weed management is one of the most important aspects of crop productivity; knowing the amount and the locations of weeds has been a problem that experts have faced for several decades. This paper presents three methods for weed estimation based on deep learning image processing in lettuce crops, and we compared them to visual estimations by experts. One method is based on support vector machines (SVM) using histograms of oriented gradients (HOG) as feature descriptor. The second method was based in YOLOV3 (you only look once V3), taking advantage of its robust architecture for object detection

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Kim, Seongje, Van-Doi Truong, Kwang-Hee Lee, and Jonghun Yoon. "Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading." Sensors 24, no. 5 (2024): 1473. http://dx.doi.org/10.3390/s24051473.

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Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parce

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Dong, Shuai, Zhihua Yang, Wensheng Li, and Kun Zou. "Dynamic Detection and Recognition of Objects Based on Sequential RGB Images." Future Internet 13, no. 7 (2021): 176. http://dx.doi.org/10.3390/fi13070176.

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Conveyors are used commonly in industrial production lines and automated sorting systems. Many applications require fast, reliable, and dynamic detection and recognition for the objects on conveyors. Aiming at this goal, we design a framework that involves three subtasks: one-class instance segmentation (OCIS), multiobject tracking (MOT), and zero-shot fine-grained recognition of 3D objects (ZSFGR3D). A new level set map network (LSMNet) and a multiview redundancy-free feature network (MVRFFNet) are proposed for the first and third subtasks, respectively. The level set map (LSM) is used to ann

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Sunwoo, Hyobin, Wonjun Choi, Seunguk Na, Cheekyeong Kim, and Seokjae Heo. "Comparison of the Performance of Artificial Intelligence Models Depending on the Labelled Image by Different User Levels." Applied Sciences 12, no. 6 (2022): 3136. http://dx.doi.org/10.3390/app12063136.

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As reconstruction and redevelopment accelerate, the generation of construction waste increases, and construction waste treatment technology is being developed accordingly, especially using artificial intelligence (AI). The majority of AI research projects fail as a consequence of poor learning data as opposed to the structure of the AI model. If data pre-processing and labeling, i.e., the processes prior to the training step, are not carried out with development purposes in mind, the desired AI model cannot be obtained. Therefore, in this study, the performance differences of the construction

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Zhang, Guocai, Guixiong Liu, and Fei Zhong. "Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Active Stereo Vision." Electronics 13, no. 24 (2024): 4872. https://doi.org/10.3390/electronics13244872.

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This study proposes an autonomous recognition and approach method for unmanned aerial vehicles (UAVs) targeting linear splicing sleeves. By integrating deep learning and active stereo vision, this method addresses the navigation challenges faced by UAVs during the identification, localization, and docking of splicing sleeves on overhead power transmission lines. First, a two-stage localization strategy, LC (Local Clustering)-RB (Reparameterization Block)-YOLO (You Only Look Once)v8n (OBB (Oriented Bounding Box)), is developed for linear target splicing sleeves. This strategy ensures rapid, acc

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Li, Junxing, Shuaiyu Li, Wenyuan Zhang, Bo Wei, and Qiyong Yang. "Experimental Measurement of Ice-Curling Stone Friction Coefficient Based on Computer Vision Technology: A Case Study of “Ice Cube” for 2022 Beijing Winter Olympics." Lubricants 10, no. 10 (2022): 265. http://dx.doi.org/10.3390/lubricants10100265.

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In the curling sport, the coefficient of friction between the curling stone and pebbled ice is crucial to predict the motion trajectory. However, the theoretical and experimental investigations on stone–ice friction are limited, mainly due to the limitations of the field measurement techniques and the inadequacy of the experimental data from professional curling rinks. In this paper, on-site measurement of the stone–ice friction coefficient in a prefabricated ice rink for the Beijing Winter Olympics curling event was carried out based on computer vision technology. Firstly, a procedure to dete

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Zhang, Jing, Jerome Maleski, Hudson Ashrafi, Jessica A. Spencer, and Ye Chu. "Open-Source High-Throughput Phenotyping for Blueberry Yield and Maturity Prediction Across Environments: Neural Network Model and Labeled Dataset for Breeders." Horticulturae 10, no. 12 (2024): 1332. https://doi.org/10.3390/horticulturae10121332.

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Time to maturity and yield are important traits for highbush blueberry (Vaccinium corymbosum) breeding. Proper determination of the time to maturity of blueberry varieties and breeding lines informs the harvest window, ensuring that the fruits are harvested at optimum maturity and quality. On the other hand, high-yielding crops bring in high profits per acre of planting. Harvesting and quantifying the yield for each blueberry breeding accession are labor-intensive and impractical. Instead, visual ratings as an estimation of yield are often used as a faster way to quantify the yield, which is c

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Schuetz, Philipp, Seline Zurfluh, and Zeno Stanga. "Mortality due to refeeding syndrome? You only find what you look for, and you only look for what you know." European Journal of Clinical Nutrition 72, no. 2 (2017): 307–8. http://dx.doi.org/10.1038/s41430-017-0018-5.

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Zhang, Jinpu, Lei Zhang, Tianyu Liu, and Yuehuan Wang. "YOLSO: You Only Look Small Object." Journal of Visual Communication and Image Representation 81 (November 2021): 103348. http://dx.doi.org/10.1016/j.jvcir.2021.103348.

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Wu, Jun W., Wei Cai, Shi M. Yu, Zhuo L. Xu, and Xue Y. He. "Optimized visual recognition algorithm in service robots." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092530. http://dx.doi.org/10.1177/1729881420925308.

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Vision-based detection methods often require consideration of the robot’s sight. For example, panoramic images cause image distortion, which negatively affects the target recognition and spatial localization. Furthermore, the original you only look once method does not have a reasonable performance for the image recognition in the panoramic images. Consequently, some failures have been reported so far when implementing the visual recognition on the robot. In the present study, it is intended to optimize the conventional you only look once algorithm and propose the modified you only look once a

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Matthews, Kylie, Sandra Capra, and Michelle Palmer. "Response to letter to the editor ‘Mortality due to refeeding syndrome? You only find what you look for, and you only look for what you know’." European Journal of Clinical Nutrition 72, no. 2 (2017): 309–10. http://dx.doi.org/10.1038/s41430-017-0025-6.

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Anorbaev, Akmal, Prerna Agarwal, and Pranav Shrivastava. "You Only Look Once (YOLO): Object Detection Algorithm." International Journal of Computer Applications ICAIDSC2023, no. 3 (2025): 9–14. https://doi.org/10.5120/icaidsc202419.

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Al-Haimi, Hamzah Abdulmalek, Zamani Md Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, Azizul Azizan, and Samsul Ariffin Abdul Karim. "Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1585. http://dx.doi.org/10.11591/ijai.v12.i4.pp1585-1592.

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<p>This project aims to develop a vision system that can detect traffic light<br />counter and to recognise the numbers shown on it. The system used you only<br />look once version 3 (YOLOv3) algorithm because of its robust performance<br />and reliability and able to be implemented in Nvidia Jetson nano kit. A total<br />of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another<br />80% (1764) from the images are used for training and 20% (440) are used for<br />testing. The results obtained from the training demonstrated Total<br /&

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Al-Haimi, Hamzah Abdulmalek, Zamani Md Sani, Tarmizi Ahmad Izzudin, Hadhrami Abdul Ghani, Azizul Azizan, and Karim Samsul Ariffin Abdul. "Traffic light counter detection comparison using you only look oncev3 and you only look oncev5 for version 3 and 5." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1585–92. https://doi.org/10.11591/ijai.v12.i4.pp1585-1592.

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This project aims to develop a vision system that can detect traffic light counter and to recognise the numbers shown on it. The system used you only look once version 3 (YOLOv3) algorithm because of its robust performance and reliability and able to be implemented in Nvidia Jetson nano kit. A total of 2204 images consisting of numbers from 0-9 green and 0-9 red. Another 80% (1764) from the images are used for training and 20% (440) are used for testing. The results obtained from the training demonstrated Total precision=89%, Recall=99.2%, F1 score=70%, intersection over union (IoU)=70.49%, me

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NAGAGOPIRAJU, Dr V., SUVARNA PINNINTI, ANJAMMA TAMMA, SAI TEJA KAJJAYAM, and KALESHAVALI KAKARLA. "ADVANCED WILD ANIMAL DETECTION AND ALERT SYSTEM USING THE YOLO V5 MODEL POWERED BY AI." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 1 (2024): 142–45. http://dx.doi.org/10.61841/turcomat.v15i1.14556.

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An advanced wild animal detection and alert system using you only look once version5 (YOLO V5) model. The system utilizes you only look once version5 (YOLO V5) object detection algorithm to identify wild animals and alert users to their presence in real-time. The system employs a camera to capture real-time video, which is then sent to a computer running you only look once version5 (YOLO V5) algorithm. When the system detects a wild animal, it sends an alert to the wild animal by playing any sounds like bullets firing. The system is expected to have a significant impact on the safety of people

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Lo, Mun Ling. "You can only see what you have chosen to see." International Journal for Lesson and Learning Studies 5, no. 3 (2016): 170–79. http://dx.doi.org/10.1108/ijlls-05-2016-0011.

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Purpose – The purpose of this paper is to suggest ways that we can widen our vision since our views are limited by our theoretical lens. Design/methodology/approach – The paper first draws on articles in the current issue to illustrate how limitations imposed by our theoretical lens can be partly overcome. It then draws on the insights from two recent papers by Svensson (2016) and Lo and Chik (2016) to discuss some ways forward. Svensson’s paper argues for integrating research on teaching and learning using case-based studies and the contextual analysis approach. Lo and Chik’s paper is about h

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Munshi, Mohseenkhan, and Sanjith Saseedharan. "Medication Reconcilation: “You will find it only if you look for it”." Indian Journal of Pharmacy Practice 8, no. 3 (2015): 98–101. http://dx.doi.org/10.5530/ijopp.8.3.3.

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d'Avoine, Pierre, and Clare Melhuish. "ONE:MORE." Architectural Research Quarterly 11, no. 2 (2007): 118–31. http://dx.doi.org/10.1017/s1359135507000590.

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‘When you go into a room and see a box of cigars, a computer and a horse you look for similarities, but if the room contains only one object you look for differences.’ (Luciano Berio, introducing a BBC Symphony Orchestra weekend of his music at The Barbican in 1990)

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Li, Xiali, Manjun Tian, Shihan Kong, Licheng Wu, and Junzhi Yu. "A modified YOLOv3 detection method for vision-based water surface garbage capture robot." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142093271. http://dx.doi.org/10.1177/1729881420932715.

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To tackle the water surface pollution problem, a vision-based water surface garbage capture robot has been developed in our lab. In this article, we present a modified you only look once v3-based garbage detection method, allowing real-time and high-precision object detection in dynamic aquatic environments. More specifically, to improve the real-time detection performance, the detection scales of you only look once v3 are simplified from 3 to 2. Besides, to guarantee the accuracy of detection, the anchor boxes of our training data set are reclustered for replacing some of the original you onl

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Wijanarko, Restu Gilang, Afu Ichsan Pradana, and Dwi Hartanti. "IMPLEMENTASI DETEKSI DRONE MENGGUNAKAN YOLO (You Only Look Once)." JURNAL FASILKOM 14, no. 2 (2024): 437–42. https://doi.org/10.37859/jf.v14i2.7374.

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Penelitian ini berfokus pada pengembangan sistem deteksi drone yang akurat dan efektif berbasis teknologi kamera. Penggunaan drone yang semakin meningkat di berbagai sektor, seperti fotografi udara, pemantauan lingkungan, pemetaan topografi, pengiriman barang, inspeksi infrastruktur, dan pertanian presisi, telah membawa banyak manfaat. Namun, penggunaan drone yang tidak bertanggung jawab juga dapat menimbulkan masalah serius, seperti pelanggaran privasi, risiko keamanan, dan gangguan pada operasi penerbangan. Untuk mengatasi tantangan ini, penelitian ini mengusulkan pendekatan deteksi drone me

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Rizqi Basuki, Nurfadjri Akbar, and Hustinawaty Hustinawaty. "You only look once v8 for fish species identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3314. http://dx.doi.org/10.11591/ijai.v13.i3.pp3314-3321.

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<p>This research aims to test the performance of you only look once (YOLOv8) in identifying fish species in Indonesian waters. Fish image data is obtained from various sources to conduct testing. The results show that YOLOv8 is able to identify fish species with a mAP accuracy rate of 97%. These results reveal the great potential of deep learning technology in supporting the preservation of marine biodiversity as well as the development of various applications, such as fisheries monitoring, conservation, and marine-based tourism development in Indonesia. With its efficient object detecti

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Prameswari, F., H. Octafiani, and T. Haryanto. "You Only Look Once (YOLOv8) for Fish Species Detection." IOP Conference Series: Earth and Environmental Science 1359, no. 1 (2024): 012023. http://dx.doi.org/10.1088/1755-1315/1359/1/012023.

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Abstract As a maritime nation characterised by vast expanses of seas, Indonesia committed to preserving and managing its abundant marine resources. Therefore, developing the fisheries industry with the ability of computers and its technology, especially in the process of fish species detection precisely. In this research, the author proposed a new model with the state-of-the-art deep learning YOLOv8s (You Only Look Once) for fish species detection. The model is trained with Fish-gres dataset to provide ground truth labels for training and evaluation purposes. Four species observed in this rese

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Arifadilah, Daffa, Asriyanik, and Agung Pambudi. "Sunda Script Detection Using You Only Look Once Algorithm." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 3, no. 2 (2024): 606–13. http://dx.doi.org/10.59934/jaiea.v3i2.443.

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The Sundanese script is a writing system used in the Sundanese language, one of the regional languages of West Java, Indonesia. This study investigates the use of the YOLO v8 algorithm for the real-time video detection of Sundanese script. Various versions of YOLO v8, including YOLO v8n, v8s, v8m, v8l, and v8x, were tested to determine the most effective model. After a comprehensive evaluation involving the analysis of mean Average Precision (mAP), F1-Confidence, and precision, the study selected the YOLO v8s model as the primary detection method. YOLO v8s demonstrated superior performance wit

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Nurfadjri, Akbar Rizqi Basuki, and Hustinawaty Hustinawaty. "You only look once v8 for fish species identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3314–21. https://doi.org/10.11591/ijai.v13.i3.pp3314-3321.

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This research aims to test the performance of you only look once (YOLOv8) in identifying fish species in Indonesian waters. Fish image data is obtained from various sources to conduct testing. The results show that YOLOv8 is able to identify fish species with a mAP accuracy rate of 97%. These results reveal the great potential of deep learning technology in supporting the preservation of marine biodiversity as well as the development of various applications, such as fisheries monitoring, conservation, and marine-based tourism development in Indonesia. With its efficient object detection and cl

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Vennebush, G. Patrick, Elizabeth Marquez, and Joseph Larsen. "Embedding Algebraic Thinking throughout the Mathematics Curriculum." Mathematics Teaching in the Middle School 11, no. 2 (2005): 86–93. http://dx.doi.org/10.5951/mtms.11.2.0086.

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Like love, algebra is where you find it. You can locate it almost anywhere in the middle school curriculum if you know where to look and what to look for. But with so many demands on our time, we often forget to look. We take problems at face value, and we assume that a geometry problem is just a geometry problem or that a data analysis activity is only about data analysis. If we scratch below the surface, however, we can find rich opportunities for algebraic thinking lurking in number explorations, measurement tasks, and geometry investigations.

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Huangfu, Zhongmin, and Shuqing Li. "Lightweight You Only Look Once v8: An Upgraded You Only Look Once v8 Algorithm for Small Object Identification in Unmanned Aerial Vehicle Images." Applied Sciences 13, no. 22 (2023): 12369. http://dx.doi.org/10.3390/app132212369.

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In order to solve the problems of high leakage rate, high false detection rate, low detection success rate and large model volume of small targets in the traditional target detection algorithm for Unmanned Aerial Vehicle (UAV) aerial images, a lightweight You Only Look Once (YOLO) v8 algorithm model Lightweight (LW)-YOLO v8 is proposed. By increasing the channel attention mechanism Squeeze-and-Excitation (SE) module, this method can adaptively improves the model’s ability to extract features from small targets; at the same time, the lightweight convolution technology is introduced into the Con

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Neville-Neil, George V. "In Praise of the Disassembler." Queue 19, no. 2 (2021): 21–28. http://dx.doi.org/10.1145/3466132.3469121.

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When you're starting out you want to be able to hold the entire program in your head if at all possible. Once you're conversant with your first, simple assembly language and the machine architecture you're working with, it will be completely possible to look at a page or two of your assembly and know not only what it is supposed to do but also what the machine will do for you step by step. When you look at a high-level language, you should be able to understand what you mean it to do, but often you have no idea just how your intent will be translated into action. Assembly and machine code is w

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Zhang, Xiaoguo, Ye Gao, Huiqing Wang, and Qing Wang. "Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection." International Journal of Advanced Robotic Systems 17, no. 4 (2020): 172988142093606. http://dx.doi.org/10.1177/1729881420936062.

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Effectively and efficiently recognizing multi-scale objects is one of the key challenges of utilizing deep convolutional neural network to the object detection field. YOLOv3 (You only look once v3) is the state-of-the-art object detector with good performance in both aspects of accuracy and speed; however, the scale variation is still the challenging problem which needs to be improved. Considering that the detection performances of multi-scale objects are related to the receptive fields of the network, in this work, we propose a novel dilated spatial pyramid module to integrate multi-scale inf

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Yang, Yang, and Hongmin Deng. "GC-YOLOv3: You Only Look Once with Global Context Block." Electronics 9, no. 8 (2020): 1235. http://dx.doi.org/10.3390/electronics9081235.

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In order to make the classification and regression of single-stage detectors more accurate, an object detection algorithm named Global Context You-Only-Look-Once v3 (GC-YOLOv3) is proposed based on the You-Only-Look-Once (YOLO) in this paper. Firstly, a better cascading model with learnable semantic fusion between a feature extraction network and a feature pyramid network is designed to improve detection accuracy using a global context block. Secondly, the information to be retained is screened by combining three different scaling feature maps together. Finally, a global self-attention mechani

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Kulkarni,, Shraddha, and Gururaj S. P. "Deep Learning Based Object Detection Using You Only Look Once." International Journal of Research in Advent Technology 7, no. 4 (2019): 9–12. http://dx.doi.org/10.32622/ijrat.74201902.

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Haval, Abhijeet Madhukar, and Md Afzal. "Aquatic object detection using YOLO (you only look once) algorithm." International Journal of Aquatic Research and Environmental Studies 4, S1 (2024): 52–57. https://doi.org/10.70102/ijares/v4s1/9.

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The programmed grouping of marine species in view of pictures is a difficult errand for which different arrangements have been progressively given in the beyond twenty years. Seas are complicated environments, hard to get to, and frequently the pictures got are of inferior quality. In such cases, creature arrangement becomes monotonous. Subsequently, it is much of the time important to apply improvement or pre-handling procedures to the pictures, prior to applying grouping calculations. The goal is to develop a deep learning system that is both extremely accurate and efficient, utilizing the Y

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Andi, Ilham, Mutmainnah Muchtar, and Jayanti Yusmah Sari. "Mask Detection Using the YOLO (You Only Look Once) Method." Jurnal Media Informasi Teknologi 1, no. 1 (2024): 1–12. http://dx.doi.org/10.69616/mit.v1i1.165.

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The COVID-19 pandemic has emphasized the importance of wearing masks as a preventive measure. To facilitate mask detection and ensure compliance, computer vision techniques have been widely utilized. This research aims to develop a mask detection system using the YOLO (You Only Look Once) method. YOLO is a real-time object detection method that provides accurate and efficient results. The proposed system utilizes a pre-trained YOLO model trained on a dataset comprising images of individuals with and without masks. The YOLO model can detect and locate faces, as well as differentiate between ind

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Maulidiansyah, Maulidiansyah, and Moh Ainol Yaqin. "Deteksi Tumpukan Sampah dengan Metode You Only Look Once (YOLO)." TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora 4, no. 2 (2023): 76–79. http://dx.doi.org/10.33650/trilogi.v4i2.6185.

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Sampah berasal dari rumah, pertanian, perkantoran, perusahaan, rumah sakit, pasar, dll. Sampah merupakan objek jelas yang bisa dilihat dengan mata telanjang akan tetapi masyarakat pura pura buta dengan sampah yang ada di hadapan matanya dikarenakan kurangnya kesadaran diri. Kurangnya kesadaran diri di masyarakat dapat menimbulkan risiko bencana alam dan penyakit. Oleh karena itu, Anda perlu mengawasi masyarakat dalam hal pembuangan sampah. Pemantauan oleh pemerintah dan dinas kebersihan diperlukan agar masyarakat sadar akan resiko sampah. Dengan adanya uraian tersebut, maka penelitian ini dila

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Wisnu Ardhi, Ovide Decroly, Tri Retnaningsih Soeprobowati, Kusworo Adi, Esa Prakasa, and Arief Rachman. "Enhanced you only look once approach for automatic phytoplankton identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3426. http://dx.doi.org/10.11591/ijai.v13.i3.pp3426-3436.

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<p>Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of

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Kumar, Sunil, Manisha Jailia, Sudeep Varshney, Nitish Pathak, Shabana Urooj, and Nouf Abd Elmunim. "Robust Vehicle Detection Based on Improved You Look Only Once." Computers, Materials & Continua 74, no. 2 (2023): 3561–77. http://dx.doi.org/10.32604/cmc.2023.029999.

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Ovide, Decroly Wisnu Ardhi, Retnaningsih Soeprobowati Tri, Adi Kusworo, Prakasa Esa, and Rachman Arief. "Enhanced you only look once approach for automatic phytoplankton identification." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3426–36. https://doi.org/10.11591/ijai.v13.i3.pp3426-3436.

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Conventionally, identifying phytoplankton species is challenging due to human taxonomical knowledge limitations. Advanced technology can overcome this problem. A novel model that accurately enhances phytoplankton detection and identification classification by combining asymmetric convolution and vision transformers (ACVIT) within the YOLOv8m framework is promoted with ACVIT-YOLO. The performance of this model surpasses the original YOLOv8m model, exhibiting a notable 2.4% enhancement in precision, 5.5% improvement in recall, and 1.1% gain in mAP 50 score. The enhanced effectiveness of ACVIT-YO

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., Damini. "Analysis of Object Detection Models." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3238–51. http://dx.doi.org/10.22214/ijraset.2024.59632.

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Abstract: Object detection plays a crucial role in today’s world, and it gives direction in the field of computer vision. Object detection is a fundamental task used to detect the object in the images. Object detection plays a significant role in various applications such as surveillance, medical images, and autonomous vehicles. The primary goal of object detection is to accurately detect the boundaries of objects of interest and classify those objects into predefined categories. This paper analyzes three common object detection models: Single Shot Multibox Detector (SSD), You Only Look Once (

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Shah, Agam. "Can You Repair What You Own?" Mechanical Engineering 140, no. 09 (2018): 37–41. http://dx.doi.org/10.1115/1.2018-sep3.

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Farmers could save time and money if they could troubleshoot tractors themselves, but have no access to the built-in software diagnostic tools that are accessible only to authorized servicepeople. The problem is so widespread that Nebraska farmers have taken it up with their state legislators, hoping to gain the legal right to access these diagnostic tools. These farmers are the latest members of a grassroots movement that is lobbying for what they call the “right to repair.” The movement’s goal is to give consumers the legal right to acquire parts and fix machines or devices that they have pu

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Triyanto, Wiwit Agus, Kusworo Adi, and Jatmiko Endro Suseno. "Detection and Tracking of Broiler Flock Movements in The Chicken Coop using YOLO." E3S Web of Conferences 448 (2023): 02064. http://dx.doi.org/10.1051/e3sconf/202344802064.

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Observation of the movement of broilers in the chicken coop is done to monitor the welfare and health condition of broilers. Currently, observing broiler flock activity in the chicken coop is generally still done conventionally, with manual observations made by farmers. But on a large scale, this observation method takes a lot of time and manpower, and is subjective. Therefore, an automatic observation system is needed that continuously monitors broiler activity, so as to increase the efficiency of farmer resources and reduce operational costs for observing broiler activity. This study develop

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Sansom, Dennis L. "“What you look hard at seems to look hard at you”: Metaphysics and the Poetry of Gerard Manley Hopkins." Journal of Aesthetic Education 55, no. 3 (2021): 33–58. http://dx.doi.org/10.5406/jaesteduc.55.3.0033.

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Abstract Gerard Manley Hopkins once said, “What you look hard at seems to look hard at you.” This phrase not only encapsulates the central emphasis of Hopkins’s poetry but also suggests a proper relationship between philosophy and art. The aesthetic experience of artworks can provide pivotal experiences for metaphysical interpretations, and I attempt to show that Hopkins’s poetry gives such a foundational and informative experience for philosophical investigations. Hopkins develops his poetic expressions based on what he calls the ability of language to inscape and ingress profound experiences

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Hall, Matt. "Geophysical Tutorial: The function of interpolation." Leading Edge 35, no. 4 (2016): 367–69. http://dx.doi.org/10.1190/tle35040367.1.

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When interpreting seismic or well data in desktop software, it's easy to forget that the data are discretely sampled in space and time. Even when you look at an apparently continuous log curve, there are really only samples, typically every 0.1524 m (6 inches). When you display seismic data as wiggle traces, there are perhaps samples only every 2 or 4 ms in the time dimension. Some software lets you turn off the interpolation so you can see the discrete data samples, as in Figure 1a .

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