Results 111 to 120 of about 12,214 (239)

Real‐Time Road Obstacle Detection System to Enhance Road Safety on African Roads

Engineering Reports, Volume 8, Issue 6, June 2026.
This study presents a cost‐effective real‐time road obstacle detection system using a YOLOv3 model optimized for deployment on resource‐constrained devices. Trained on African road‐specific datasets, the system improves safety by detecting vehicles, wildlife, and pedestrians, achieving high accuracy with reduced inference time and memory usage ...
Pison Mutabarura, Nicasio Maguu Muchuka, Davies Rene Segera   +2 more
wiley   +1 more source

Artificial intelligence‐assisted detection of epileptic spasms using electroencephalographic–video analysis

Epilepsia, Volume 67, Issue 6, Page 3009-3022, June 2026.
Abstract Objective This study was undertaken to develop and validate an artificial intelligence (AI) diagnostic tool using hybrid electroencephalographic (EEG)–video signals for automatic epileptic spasms (ES) detection. Methods This retrospective cohort study with internal cross‐validation and multicenter external validation was conducted from July ...
Lin Wan, Nan Lin, Wen Wang, Zi Liang, Yisu Dong, Haibo He, Jian Chen, Yuying Fan, Tong Liu, Hongjie Wang, Weike Cheng, Guangshuang Lu, Juan Wang, Bo Zhang, Qiang Lu, Guang Yang   +15 more
wiley   +1 more source

Survey on AI‐Enabled Computer Vision Technologies and Applications for Space Robotic Missions

Journal of Field Robotics, Volume 43, Issue 4, Page 2553-2584, June 2026.
ABSTRACT This survey provides a comprehensive overview of recent advancements and challenges in Artificial Intelligence (AI)‐enabled computer vision (CV) techniques for space robotic missions, spanning critical phases such as Entry, Descent, and Landing (EDL), orbital operations, and planetary surface exploration.
Maciej Quoos, Steven Kay, Julia Wajoras, Robert Field, Evridiki V. Ntagiou, Fakher Mohammad, Yang Gao   +6 more
wiley   +1 more source

ВПЛИВ РОЗДІЛЬНОЇ ЗДАТНОСТІ ВХІДНИХ ЗОБРАЖЕНЬ НА ПАРАМЕТРИ МОДЕЛЕЙ YOLO ПРИ ДЕТЕКТУВАННІ ОБ’ЄКТІВ

Автоматизация технологических и бизнес-процессов
У статті представлено результати дослідження впливу роздільної здатності вхідних зображень на ключові параметри моделей глибокого навчання YOLOv5 і YOLOv8 при виконанні завдань детектування об’єктів.
Юрій Романович Щебель
doaj   +1 more source

Application of deep learning in crop research: From genomics to phenomics

The Plant Genome, Volume 19, Issue 2, June 2026.
Abstract Deep learning, as a pivotal branch of machine learning, has demonstrated remarkable potential in advancing crop science by effectively integrating genomics and phenomics. This review systematically outlines the application of diverse deep learning architectures—such as convolutional neural networks, recurrent neural networks, and transformers ...
Zefeng Wu, Yali Sun, Qian Luo, Jiaping Wei, Junmei Cui, Yan Fang, Yining Niu, Zhaohong Li, Xiaolin Wang, Zigang Liu   +9 more
wiley   +1 more source

Drone‐Based Inspection of Wind Turbine Blades: A Comparative Study of Deep Learning Models

Wind Energy, Volume 29, Issue 6, June 2026.
ABSTRACT Maintaining wind turbine blades is a challenging task, often marked by high costs, safety risks, time inefficiency, and the possibility of incorrect diagnosis. A promising approach to support preventive maintenance involves the use of drones and deep learning for inspection and early fault detection.
Lakhdar Laib, Thameur Obaidi, Mohamed Elbar, Toufik Tayeb Naas, Ahmed Bensaci, Leliso Hobicho Dabelo   +5 more
wiley   +1 more source

YOLOv5s-TC: An Improved Intelligent Model for Insulator Fault Detection Based on YOLOv5s

Sensors
Insulators play a pivotal role in power grid infrastructure, offering indispensable electrical insulation and mechanical support. Precise and efficient detection of insulator faults is of paramount importance for safeguarding grid reliability and ensuring operational safety.
Yingying Yin, Yunpeng Duan, Xin Wang, Shuo Han, Chengyang Zhou   +4 more
openaire   +3 more sources

Quantized alpha–WIoU YOLOv5 algorithm for rice leaf disease detection

Tạp chí Khoa học và Công nghệ
This paper presents an innovative method based on convolutional neural networks for early detection and diagnosis of diseases in rice plants. The YOLOv5n model is modified by replacing its original loss function with the alpha-WIoU function to enhance ...
Nguyen Thu Ha, Vu Duc Long, Nguyen Hung Cuong, Mai Ngoc Tam, Bui Dang Thanh   +4 more
doaj   +1 more source

Corrugation and Squat Classification and Detection with VGG16 and YOLOv5 Neural Network Models

JOIV: International Journal on Informatics Visualization
Railway track defects in Malaysia pose significant risks of train derailments and accidents, underscoring the urgency for early and accurate defect detection and classification.
Muhammad Syukri Mohd Yazed, Mohd Amin Mohd Yunus, Ezak Fadzrin Ahmad Shaubari, Nor Aziati Abdul Hamid, Azmale Amzah, Zulhelmi Md Ali   +5 more
doaj   +1 more source

Mendeteksi Kendaraan menggunakan Algoritma YoloV5 [PDF]

Dengan kemajuan dalam bidang teknologi yang berkembang begitu pesat dimasa kini, seperti contoh dalam bidang Object detection atau Pendeteksi Objek. Seiring dengan berkembangnya ilmu teknologi saat ini, kebutuhan akan sistem Object detection juga akan ...
Amiennullah, Jefri
core   +1 more source