Suchergebnisse - "Object detection algorithm"

Automatic detection of trapping events of postnatal piglets in loose housing pen: comparison of YOLO versions 4, 5, and 8

Autoren: Taeyong Yun Jinsul Kim Jinhyeon Yun et al.

Quelle: J Anim Sci Technol
Journal of Animal Science and Technology, Vol 67, Iss 3, Pp 666-676 (2025)

Schlagwörter: You Only Look Once (YOLO), Artificial intelligence of things (AIoT), Trapping, Piglet crushing, Deep learning object-detection algorithm, SF1-1100, Animal culture, Research Article

Zugangs-URL: https://doaj.org/article/f007a03f0f4142caa31496cbd274bfbf

Aircraft Skin Damage Visual Testing System Using Lightweight Devices with YOLO: An Automated Real-Time Material Evaluation System

Autoren: Kuo-Chien Liao Jirayu Lau Muhamad Hidayat

Quelle: AI, Vol 5, Iss 4, Pp 1793-1815 (2024)

Schlagwörter: IoT, Electronic computers. Computer science, YOLOv9, UAV application, automated inspection, object detection algorithm, QA75.5-76.95, real-time detection

Zugangs-URL: https://doaj.org/article/55e0de2070cd49169c7ef76f1d2e44c2

Investigating safety-related behavior of e-scooter riders using computer vision algorithms

Autoren: Siebert, Felix Wilhelm Ringhand, Madlen Anke, Juliane et al.

Weitere Verfasser: Siebert, Felix Wilhelm Ringhand, Madlen Anke, Juliane et al.

Schlagwörter: E-Scooter Safety, Video-based Observation, Risk-increasing Behaviors, Object Detection Algorithm, Time-of-day Effects, E-Scooter-Sicherheit, videobasierte Beobachtung, risikoerhöhende Verhaltensweisen, Objekterkennungsalgorithmus, tageszeitliche Effekte, ddc:380

Zugangs-URL: https://tud.qucosa.de/id/qucosa:97242
https://tud.qucosa.de/api/qucosa:97242/attachment/ATT-0/

Target detection technology of coal mine wheeled robot combining improved CNN and self attention mechanism

Autoren: Junfei TANG Hailong XING Su LI et al.

Quelle: Meikuang Anquan, Vol 56, Iss 3, Pp 224-232 (2025)

Schlagwörter: coal mine wheeled robot, intelligent inspection, object detection algorithm, pyramid structure, attention mechanism, deep learning, Mining engineering. Metallurgy, TN1-997

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1003-496X

Zugangs-URL: https://doaj.org/article/3f410516ebb14b75ac72937d120c9a46

A 5G network based conceptual framework for real-time malaria parasite detection from thick and thin blood smear slides using modified YOLOv5 model

Autoren: Swati Lipsa Ranjan Kumar Dash Korhan Cengiz et al.

Quelle: Digit Health
Digital Health, Vol 11 (2025)

Schlagwörter: GIoU, automated malaria detection, Computer applications to medicine. Medical informatics, R858-859.7, 5G network, object detection algorithm, Original Research Article, mean average precision, YOLO model, e-Healthcare, CNN model

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40078448
https://doaj.org/article/d8853fee124442c68ed134c3b420f98c

A Review of Vehicle Detection Methods Based on Computer Vision

Autoren: Changxi Ma Fansong Xue

Quelle: Journal of Intelligent and Connected Vehicles, Vol 7, Iss 1, Pp 1-18 (2024)

Schlagwörter: intelligent transportation system, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, deep learning, vehicle detection, TL1-4050, object detection algorithm, 02 engineering and technology, computer vision, Motor vehicles. Aeronautics. Astronautics

Zugangs-URL: https://doaj.org/article/2ac2f29766404a3e916f3016cc261575

Design and Research of an Intelligent Detection Method for Coal Mine Fire Edges

Autoren: Yingbing Yang Duan Zhao Yicheng Ge et al.

Quelle: Applied Sciences, Vol 15, Iss 19, p 10589 (2025)

Schlagwörter: fire external cause detection, YOLOv5s object detection algorithm, multi-source information fusion, adaptive attention mechanism, edge of computing, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/15/19/10589; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/e0375106c0574528a5b165d57e03b936

Sustainable Transportation Design: Examining the Application Effect of Auxiliary Lanes on Dual-Lane Exit Ramps on Chinese Freeways

Autoren: Yutong Liu Zhipeng Fu Yiyun Ma et al.

Quelle: Sustainability ; Volume 17 ; Issue 4 ; Pages: 1533

Schlagwörter: freeway, auxiliary lane, applicable conditions, traffic flow theory, VISSIM simulation, object detection algorithm, sustainable transportation design

Geographisches Schlagwort: agris

Dateibeschreibung: application/pdf

Relation: https://dx.doi.org/10.3390/su17041533

Verfügbarkeit: https://doi.org/10.3390/su17041533

Deep-learning-based gestational sac detection in ultrasound images using modified YOLOv7-E6E model

Autoren: Tae-kyeong Kim Jin Soo Kim Hyun-chong Cho

Quelle: J Anim Sci Technol
Journal of Animal Science and Technology, Vol 65, Iss 3, Pp 627-637 (2023)

Schlagwörter: 2. Zero hunger, Sow, Ultrasound, Pig sac, Deep learning, Object-detection algorithm, SF1-1100, Animal culture, Research Article

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37332278
https://doaj.org/article/72e248a60e4e48a0aebcaed9d1ad0c8b

Enhancing Bee Mite Detection with YOLO: The Role of Data Augmentation and Stratified Sampling

Autoren: Hong-Gu Lee Jeong-Yong Shin Su-Bae Kim et al.

Quelle: Agriculture, Vol 15, Iss 11, p 1221 (2025)

Schlagwörter: bee mite detection, YOLO object detection algorithm, data augmentation, stratified sampling, beekeeping imaging system, Agriculture (General), S1-972

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2077-0472/15/11/1221; https://doaj.org/toc/2077-0472

Zugangs-URL: https://doaj.org/article/6199571405294464864d12a6ffc10f52

Classification of Fritillaria thunbergii appearance quality based on machine vision and machine learning technology

Autoren: DONG Chengye LI Dongfang FENG Huaiqu et al.

Quelle: 浙江大学学报. 农业与生命科学版, Vol 49, Pp 881-892 (2023)

Schlagwörter: Fritillaria thunbergii, statistical learning, deep learning, object detection, object detection algorithm YOLO-X, dilated convolution, Biology (General), QH301-705.5, Agriculture (General), S1-972

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1008-9209; https://doaj.org/toc/2097-5155

Zugangs-URL: https://doaj.org/article/29e5c071e77f448f9caa5a0591646050

Recognizing Mangoes and Determining their Ripeness Through the Application of Image Processing and Machine Learning Techniques

Autoren: Gavali, Ashwini B. Thigale, Somnath B. Togrikar , Pradeep S. et al.

Quelle: International Journal of Intelligent Systems and Applications in Engineering; Vol. 12 No. 19s (2024); 624-629

Schlagwörter: Machine Learning, Object detection algorithm, Cost-efficient, Faster R-CNN, Image Processing, Ripeness

Dateibeschreibung: application/pdf

Zugangs-URL: https://www.ijisae.org/index.php/IJISAE/article/view/5106

EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection

Autoren: Haojie Chen Lumei Su Riben Shu et al.

Quelle: Applied Sciences, Vol 14, Iss 21, p 9779 (2024)

Schlagwörter: isolation switch detection, lightweight object detection algorithm, attention mechanism, efficient mobile inverted bottleneck convolution, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Dateibeschreibung: electronic resource

Relation: https://www.mdpi.com/2076-3417/14/21/9779; https://doaj.org/toc/2076-3417

Zugangs-URL: https://doaj.org/article/03fe9b39ed2f4445ab5b6c2e6bff9e20

A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm

Autoren: Jinshan Sun Ronghuan Zheng Xuan Liu et al.

Quelle: Sensors, Vol 24, Iss 19, p 6339 (2024)

Schlagwörter: explosive transport vehicle, YOLOv4 object detection algorithm, cellular automata, intelligent recognition, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/24/19/6339; https://doaj.org/toc/1424-8220; https://doaj.org/article/91878bb521cf4f47878df33d4cfd0ce7

Verfügbarkeit: https://doi.org/10.3390/s24196339
https://doaj.org/article/91878bb521cf4f47878df33d4cfd0ce7

Multi-Module Fusion Model for Submarine Pipeline Identification Based on YOLOv5

Autoren: Bochen Duan Shengping Wang Changlong Luo et al.

Quelle: Journal of Marine Science and Engineering, Vol 12, Iss 3, p 451 (2024)

Schlagwörter: subsea pipelines, pipeline detection, sub-bottom profiler (SBP) system, YOLOv5 object detection algorithm, Naval architecture. Shipbuilding. Marine engineering, VM1-989, Oceanography, GC1-1581

Relation: https://www.mdpi.com/2077-1312/12/3/451; https://doaj.org/toc/2077-1312; https://doaj.org/article/2da877d5068a42d5b2f67f7175b0abd3

Verfügbarkeit: https://doi.org/10.3390/jmse12030451
https://doaj.org/article/2da877d5068a42d5b2f67f7175b0abd3

Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

Autoren: Zhibin Ma Yanqi Dong Yi Xia et al.

Quelle: Remote Sensing, Vol 16, Iss 8, p 1350 (2024)

Schlagwörter: real-time detection, forest wildlife, object detection algorithm, Science

Relation: https://www.mdpi.com/2072-4292/16/8/1350; https://doaj.org/toc/2072-4292; https://doaj.org/article/077ea02aa2284d4491ff7786b4f42bc5

Verfügbarkeit: https://doi.org/10.3390/rs16081350
https://doaj.org/article/077ea02aa2284d4491ff7786b4f42bc5

Object Detection-Tracking Algorithm for Unmanned Surface Vehicles Based on a Radar-Photoelectric System

Autoren: Qingze Yu Bo Wang Yumin Su

Quelle: IEEE Access, Vol 9, Pp 57529-57541 (2021)

Schlagwörter: 03 medical and health sciences, radar-photoelectric system, 0302 clinical medicine, Unmanned surface vehicle, 0202 electrical engineering, electronic engineering, information engineering, object tracking algorithm, object detection algorithm, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, ROI-prediction algorithm, first-frame object extraction algorithm, TK1-9971

Zugangs-URL: https://ieeexplore.ieee.org/ielx7/6287639/9312710/09402914.pdf
https://doaj.org/article/caa2f5ccd45b4b8bb77dec5f336a107b
https://ieeexplore.ieee.org/document/9402914
https://doi.org/10.1109/ACCESS.2021.3072897
https://dblp.uni-trier.de/db/journals/access/access9.html#YuWS21

Core ML-based Aircraft Skin Damage Detection System Using YOLO11

Autoren: 廖國健 Liao, Kuo-Chien Lau, Jirayu et al.

Weitere Verfasser: 廖國健 Liao, Kuo-Chien Lau, Jirayu et al.

Schlagwörter: Automated inspection, YOLO11, UAV application, Object detection algorithm, Aircraft structural damage detection

Dateibeschreibung: 102 bytes; text/html

Relation: 2024 6th International Conference on Control and Robotics (ICCR) / December 5-7, 2024 Yokohama, Japan、 Page(s):355 - 360; http://ir.lib.cyut.edu.tw:8080/handle/310901800/44378; http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/44378/2/index.html

Verfügbarkeit: http://ir.lib.cyut.edu.tw:8080/handle/310901800/44378
http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/44378/2/index.html

Corridor-Wide Surveillance Using Unmanned Aircraft Systems

Autoren: Zhang, Yu Kourtellis, Achilleas Post, Joseph et al.

Schlagwörter: traffic, transportation, drones, experimental design, UAV, UAS applications, unmanned aircraft system, object detection algorithm, incident management

Dateibeschreibung: application/pdf

Take Back the Algorithms! A Media Theory of Commonistic Affordance

Autoren: Miyazaki, Shintaro PETIT, Maximilien

Weitere Verfasser: Miyazaki, Shintaro PETIT, Maximilien

Quelle: Media Theory, Vol 3, Iss 1 (2019)

Schlagwörter: Affordance, Media, Violaâ€'Jones object detection algorithm, Viola-Jones object detection algorithm, Practice- oriented critical media studies, Practice-oriented critical media studies, Communication. Mass media, Media theory, Commoning, [SHS] Humanities and Social Sciences, P87-96, [SHS.INFO] Humanities and Social Sciences/Library and information sciences

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/89085abead2c4fdaaae19020d2854b81
http://journalcontent.mediatheoryjournal.org/index.php/mt/article/download/89/79
http://journalcontent.mediatheoryjournal.org/index.php/mt/article/view/89
https://hal.science/hal-02470429v1