Results 61 to 70 of about 7,696 (200)

Advancing collagen‐related pathology assessment through second‐harmonic generation imaging

Journal of Microscopy, EarlyView.
Abstract Collagen remodelling and dysregulation are the hallmarks of diverse pathological conditions. In this context, second‐harmonic generation (SHG) imaging has emerged as a powerful label‐free modality for assessing collagen. This offers submicron resolution, intrinsic optical sectioning, and deeper imaging capabilities without the need for ...
Jackson Rodrigues, Manikanth Karnati, Gagan Raju, Edwin Pious, Ankur Gogoi, Guan‐Yu Zhuo, Nirmal Mazumder, Fu‐Jen Kao   +7 more
wiley   +1 more source

From Detection to Inspection: A Virtual Reference Framework for Automated Road Marking Degradation Assessment

Applied Sciences
Ensuring the visibility of road markings is critical for traffic safety, yet current inspection methods remain either prohibitively expensive (retroreflectivity) or subjective (manual assessment).
Térence Bordet, Maxime Redondin, Stefan Bornhofen, Sébastien Denaës, Aymeric Histace   +4 more
doaj   +1 more source

Artificial Intelligence in Periodontology: A Systematic Review

Journal of Periodontal Research, EarlyView.
AI shows promise across periodontology, with deep learning achieving strong performance for image‐based diagnosis of periodontitis. However, limited data diversity, inconsistent metrics, and scarce external validation raise concerns about generalizability and clinical applicability.
Antonin Tichy, Nils Werner, Helena Dujic, Charlotte Wetzel, Vinay Pitchika, Caspar Victor Bumm, Matthias Folwaczny, Falk Schwendicke   +7 more
wiley   +1 more source

Road Damage Detection for Autonomous Driving Vehicles using YOLOv8 and Salp Swarm Algorithm

Applications of Modelling and Simulation
Road accidents are one of the leading causes of death and serious injury in Malaysia, often resulting from human errors and poor road conditions. Autonomous vehicles aim to reduce accidents by mitigating human errors. Therefore, improving the road damage
Nik Ahmad Farihin Mohd Zulkifli, Zuriani Mustaffa, Mohd Herwan Sulaiman   +2 more
doaj  

YOLOV8-MR: An Improved Lightweight YOLOv8 Algorithm for Tomato Fruit Detection

IEEE Access
As one of the widely planted fruit and vegetable crops, real-time monitoring of the tomato growth process is crucial for enhancing production and maintaining quality. To fulfill the requirement for real-time detection, we introduce an enhanced tomato target detection approach utilizing the YOLOv8n model, offering essential technical assistance for both
Xu Li, Changhan Cai, Yue Yang 0015, Bo Song   +3 more
openaire   +2 more sources

FieldDino: Rapid In‐Field Stomatal Anatomy and Physiology Phenotyping

Plant, Cell &Environment, EarlyView.
ABSTRACT Stomatal anatomy and physiology define CO2 availability for photosynthesis and regulate plant water use. Despite being key drivers of yield and dynamic responsiveness to abiotic stresses, conventional measurement techniques of stomatal traits are laborious and slow, limiting adoption in plant breeding.
Edward Chaplin, Guy Coleman, Andrew Merchant, William Salter   +3 more
wiley   +1 more source

Web-based Timber Logs Information System Using the YOLOv8 Model: IstifTakip

Düzce University Faculty of Forestry Journal of Forestry
This study introduces İstifTakip, a web-based information system developed for the automated detection and measurement of stacked timber logs using the YOLOv8 deep learning model.
Remzi Eker, Kamber Can Alkiş
doaj   +1 more source

Artificial intelligence‐powered plant phenomics: Progress, challenges, and opportunities

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract Artificial intelligence (AI), a key driver of the Fourth Industrial Revolution, is being rapidly integrated into plant phenomics to automate sensing, accelerate data analysis, and support decision‐making in phenomic prediction and genomic selection.
Xu Wang, Daeun Choi, Damian Adams, Jaehyun Ahn, Kelly Balmant, Raquel Dias, Carlos D. Messina, Rafael Muñoz‐Carpena, Vance M. Whitaker, Haipeng Yu, Ziwen Yu, Chang Zhao, Changying Li   +12 more
wiley   +1 more source

Study on Spark Image Detection for Abrasive Belt Grinding via Transfer Learning with YOLOv8

Sensors
Aiming to solve the problems of low precision and poor efficiency caused by relying on manual experience during the manual polishing of blades, a multi-view spark image detection method based on YOLOv8 transfer learning is proposed.
Jian Huang, Guangpeng Zhang
doaj   +1 more source

CFE-YOLOv8s: Improved YOLOv8s for Steel Surface Defect Detection

Electronics
Due to the low detection accuracy in steel surface defect detection and the constraints of limited hardware resources, we propose an improved model for steel surface defect detection, named CBiF-FC-EFC-YOLOv8s (CFE-YOLOv8s), including CBS-BiFormer (CBiF) modules, Faster-C2f (FC) modules, and EMA-Faster-C2f (EFC) modules.
Shuxin Yang, Yang Xie, Jianqing Wu, Weidong Huang, Hongsheng Yan, Jingyong Wang, Bi Wang, Xiangchun Yu, Qiang Wu, Fei Xie   +9 more
openaire   +1 more source