Results 201 to 210 of about 19,743 (267)

YOLO‐Based Detection and Localization of Rocket Induced Traveling Ionospheric Disturbances

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract During rocket launches, atmospheric disturbances significantly impact the ionosphere, generating Traveling Ionospheric Disturbances (TIDs). These disturbances can affect radio communications and navigation systems and may pose risks to high‐speed aerospace vehicles.
Zhou Chen, Jiahao Zeng, Jing‐Song Wang, Yan Guo, Cunqun Fan, Fabao Yan, Hui Zhu   +6 more
wiley   +1 more source

TI-YOLO: A Lightweight and Efficient Anatomical Structure Detection Model for Tracheal Intubation. [PDF]

Bioengineering (Basel)
Tian Y, Yang C, Sang L, Ping C, Feng L, Chen W, Wang H, Li W, Han Y.   +8 more
europepmc   +1 more source

Automated Mineral Identification and Rock‐Type Classification of Lunar Mare Basalts Using SEM Images

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 4, August 2026.
Abstract We present an automated system for identifying minerals and classifying rock types in Apollo lunar mare basalts using scanning electron microscopy (SEM) imagery. Mineral segmentation is based on a U‐Net architecture, supplemented by two scale‐aware models designed to incorporate pixel size information.
Ji‐In Jung, Sonia M. Tikoo, Jaehong Chung, Claire I. O. Nichols   +3 more
wiley   +1 more source

YOLO-BP for detection of multi-scale and high intra-class variation electrode cap defects in resistance spot welding. [PDF]

PLoS One
Zhao X, Leong SH, Lee BY, Mutalib S, Qiu B, Lyu M, Zuo Z.   +6 more
europepmc   +1 more source

Localization and Classification of Gravity Wave Events From VIIRS Day/Night Band Satellite Imagery Using Machine Learning Techniques

Earth and Space Science, Volume 13, Issue 8, August 2026.
Abstract Gravity waves in the upper mesosphere produce diverse spatial patterns in nighttime airglow, but their global morphology has been difficult to characterize because manual identification is impractical for the massive satellite image archive. We developed a machine learning framework to detect and classify gravity wave events in imagery from ...
Yuta Hozumi, Jia Yue, Seraj Al Mahmud Mostafa, Chenxi Wang, Jianwu Wang, Sanjay Purushotham, Steven D. Miller   +6 more
wiley   +1 more source

RFE-YOLO: A Lightweight Receptive Field-Enhanced Network for UAV Imagery Object Detection. [PDF]

Sensors (Basel)
Peng Y, Ge X.
europepmc   +1 more source

RAPID: Real‐time animal pattern re‐identification on edge devices, an open‐source tool for field deployment

Methods in Ecology and Evolution, Volume 17, Issue 8, Page 2379-2394, August 2026.
Abstract Automatic re‐identification of animals has significant potential to address pressing ecological and conservation challenges through improved population monitoring, individual health assessment and detailed behavioural analyses. Although numerous computer‐vision‐based solutions have been proposed and many achieve high accuracy, most remain ...
András Zábó, Robinson Chaquinga, Jaime Palacios Pérez, Daniel Rubenstein, Máté Nagy, Aamir Ahmad   +5 more
wiley   +1 more source

YOLO-MDEW:Improved YOLOv8 for application of wood board edge banding defect detection. [PDF]

PLoS One
Xiao E, Zhou R, Ruan Z, Chien CJT, Zhu J.   +4 more
europepmc   +1 more source

Synthetic Seismic Data Generation for Deep Learning Segmentation of Clinoforms: Integrating Process‐Based Forward Stratigraphic Modelling and Ray‐Based Seismic Modelling

Basin Research, Volume 38, Issue 4, July–August 2026.
This study aims to develop a deep learning model for automated clinoform segmentation. The trained model achieved high segmentation performance on a synthetic testing dataset and successfully generalised to real seismic profiles. The proposed approach demonstrates the effectiveness of using forward stratigraphic modelling and point spread function for ...
Waleed AlGharbi, Rebecca E. Bell, Cédric John   +2 more
wiley   +1 more source