Suchergebnisse - YOLOv3 object detection algorithm

Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm

Autoren: Dafa Li Huanlong Liu Tao Wei et al.

Quelle: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 236:1780-1795

Schlagwörter: 0209 industrial biotechnology, 02 engineering and technology

Zugangs-URL: https://journals.sagepub.com/doi/10.1177/09544062211019774
http://journals.sagepub.com/doi/10.1177/09544062211019774

Model Compression Based on YOLOv3 Object Detection Algorithm from the Perspective of UAV

Autoren: Wenjie Chen Bing Wu Zhijun Xue

Quelle: 2021 40th Chinese Control Conference (CCC). :8439-8444

Schlagwörter: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: http://xplorestaging.ieee.org/ielx7/9549237/9549238/09550707.pdf?arnumber=9550707
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9550707

An Improved Yolov3 Object Detection Algorithm for UAV Aerial Images

Autoren: Zhihong Zhang Huajun Gong Xinhua Wang et al.

Quelle: 2021 International Conference on Intelligent Computing, Automation and Applications (ICAA). :542-550

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Application of a Model that Combines the YOLOv3 Object Detection Algorithm and Canny Edge Detection Algorithm to Detect Highway Accidents

Autoren: Yao-Liang Chung Chuan-Kai Lin

Quelle: Symmetry; Volume 12; Issue 11; Pages: 1875

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, YOLOv3, Canny, object detection, accident detection, artificial intelligence, 02 engineering and technology

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.mdpi.com/2073-8994/12/11/1875/pdf
https://www.mdpi.com/2073-8994/12/11/1875/pdf
http://ui.adsabs.harvard.edu/abs/2020Symm...12.1875C/abstract
https://dblp.uni-trier.de/db/journals/symmetry/symmetry12.html#ChungL20
https://www.mdpi.com/2073-8994/12/11/1875
https://doi.org/10.3390/sym12111875

In-vehicle object detection with YOLO algorithm

Autoren: Farinha, João Simões Cortez, Paulo Ferreira, André Leite et al.

Weitere Verfasser: Farinha, João Simões Cortez, Paulo Ferreira, André Leite et al.

Quelle: urn:tid:202348156

Dateibeschreibung: application/pdf

Verfügbarkeit: https://hdl.handle.net/1822/64273

Individual behavior tracking of heifers by using object detection algorithm YOLOv4.

Autoren: Jahn, Sarah Schmidt, Gabriel Bachmann, Lisa et al.

Quelle: Frontiers in Animal Science. 2025, p1-11. 11p.

Comparison classification algorithms and the YOLO method for video analysis and object detection.

Autoren: Magdin M Balogh Z

Quelle: Scientific reports [Sci Rep] 2025 Jul 14; Vol. 15 (1), pp. 25432. Date of Electronic Publication: 2025 Jul 14.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE

Nanomesh‐YOLO: Intelligent Colorimetry E‐Skin Based on Nanomesh and Deep Learning Object Detection Algorithm.

Autoren: Chen, Hongyu Xu, Siye Liu, Haidong et al.

Quelle: Advanced Functional Materials. Feb2024, Vol. 34 Issue 8, p1-9. 9p.

Schlagwörter: *OBJECT recognition (Computer vision), *COLORIMETRY, *DEEP learning, *ALGORITHMS, *FINITE element method, *DATABASES, *MICROFLUIDICS, *ARTIFICIAL skin

Automatic Measurement of Road Traffic Volumes and Vehicle Trajectories Using an Object Detection Algorithm YOLOv3 / 物体検出アルゴリズムYOLOv3を用いた交通量と車両走行軌跡の自動計測

Autoren: 中島 浩平 倉本 俊昌 北島 俊孝 et al.

Quelle: Record of Joint Conference of Electrical and Electronics Engineers in Kyushu. 2019, :61

Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection.

Autoren: Zhu, Chunhua Liang, Jiarui Zhou, Fei

Quelle: Information. Oct2023, Vol. 14 Issue 10, p560. 16p.

Schlagwörter: Object recognition (Computer vision), Weight training, Transfer of training

YOLOv3 Object Detection Algorithm with Feature Pyramid Attention for Remote Sensing Images

Autoren: Jingguo Lv Ning-ning Qu Zhe Cheng et al.

Quelle: Sensors and Materials. 32:4537

Schlagwörter: 0211 other engineering and technologies, 0401 agriculture, forestry, and fisheries, 04 agricultural and veterinary sciences, 02 engineering and technology

Zugangs-URL: https://myukk.org/SM2017/sm_pdf/SM2427.pdf

YOLOv3 Object Detection Algorithm with Feature Pyramid Attention for Remote Sensing Images.

Autoren: Zhe Cheng Jingguo Lv Anqi Wu et al.

Quelle: Sensors & Materials; 2020, Vol. 32 Issue 12, Part 4, p4537-4558, 22p

Schlagwörter: REMOTE sensing, PYRAMIDS, OPTICAL remote sensing, BASEBALL fields, STORAGE tanks, ALGORITHMS

采用改进YOLOv3 算法检测青皮核桃.

Alternate Title: Detection of green walnut by improved YOLOv3.

Autoren: 郝建军 邴振凯 杨淑华 et al.

Quelle: Transactions of the Chinese Society of Agricultural Engineering. 2022, Vol. 38 Issue 14, p183-190. 8p.

Schlagwörter: *OBJECT recognition (Computer vision), *COMPUTER vision, *ORCHARD management, *DATA augmentation, *K-means clustering, *WALNUT

Personalized advertisement using deep learning-based object detection algorithms

Autoren: ULUTAŞ, Enes CENGİZ, Hüseyin YAZICIOĞLU, Musa et al.

Quelle: Volume: 24, Issue: 1 10-28
Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi
Journal of Balıkesir University Institute of Science and Technology

Schlagwörter: Engineering, Nesne tespiti, kişiye özgü reklam, derin öğrenme, Mühendislik, Object detection, personalized advertisement, deep learning

Dateibeschreibung: application/pdf

Zugangs-URL: https://dergipark.org.tr/tr/pub/baunfbed/issue/65201/878224

Research on Intelligent Trash Can Garbage Classification Scheme Based on Improved YOLOv3 Target Detection Algorithm.

Autoren: Wang, Ying Muthu, BalaAnand Anbarasan, M.

Quelle: Journal of Interconnection Networks. 2022Supplement, Vol. 22, p1-20. 20p.

Schlagwörter: *REFUSE containers, *WASTE management, *ALGORITHMS, OBJECT recognition (Computer vision), DEEP learning, PEOPLE with visual disabilities, ASSISTIVE technology

An advanced YOLOv3 method for small object detection.

Autoren: Liu, Baokai He, Fengjie Du, Shiqiang et al.

Quelle: Journal of Intelligent & Fuzzy Systems. 2023, Vol. 45 Issue 4, p5807-5819. 13p.

Schlagwörter: *PROBLEM solving, OBJECT recognition (Computer vision), AUTONOMOUS vehicles

Improved YOLOv3 Remote Sensing Target Detection Based on Improved Dense Connection and Distributional Ranking Loss

Autoren: YUAN Lei, LIU Zi-yan, ZHU Ming-cheng, MA Shan-shan, CHEN Lin-zhou-ting

Quelle: Jisuanji kexue, Vol 48, Iss 9, Pp 168-173 (2021)

Schlagwörter: remote sensing image, object detection, yolov3, baseline, sample imbalance, Computer software, QA76.75-76.765, Technology (General), T1-995

Dateibeschreibung: electronic resource

Relation: http://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2021-9-168.pdf; https://doaj.org/toc/1002-137X

Zugangs-URL: https://doaj.org/article/c627dc4828de4c0ab5d3adac0d700764

Road Object Detection of YOLO Algorithm with Attention Mechanism

Autoren: Huazhang Wang Yuan Xu Fan Liu et al.

Quelle: Frontiers in Signal Processing. 5

Schlagwörter: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

A Robot System for Rapid and Intelligent Bridge Damage Inspection Based on Deep-Learning Algorithms.

Autoren: Meng, Qingling Yang, Jiabing Zhang, Yun et al.

Quelle: Journal of Performance of Constructed Facilities; Dec2023, Vol. 37 Issue 6, p1-14, 14p

Schlagwörter: BRIDGE inspection, DEEP learning, OBJECT recognition (Computer vision), MACHINE learning, IMAGE segmentation, ALGORITHMS, TENDONS (Prestressed concrete)

Robotic grasping method of bolster spring based on image-based visual servoing with YOLOv3 object detection algorithm.

Autoren: Li, Dafa Liu, Huanlong Wei, Tao et al.

Quelle: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (Sage Publications, Ltd.); Feb2022, Vol. 236 Issue 3, p1780-1795, 16p