Results 61 to 70 of about 6,685 (188)

C4D-YOLOv8: improved YOLOv8 for object detection on drone-captured images

Signal, Image and Video Processing
Abstract Unmanned aerial vehicles (UAVs), as an emerging technology with vast application prospects, produce distinctive images due to factors, such as their shooting environment, altitude, and equipment. These UAV-captured images possess unique characteristics compared with traditional pictures, including high resolution, abundant presence of ...
Weihui Zeng, Pengcheng Wu, Junyan Wang, Gensheng Hu, Jinling Zhao   +4 more
openaire   +1 more source

Diagnostic Accuracy of Deep Learning Models in Detecting Peri‐Implant Marginal Bone Loss: A Systematic Review and Meta‐Analysis

Clinical Oral Implants Research, EarlyView.
ABSTRACT Background Peri‐implantitis is a common implant complication requiring early detection to prevent bone loss and implant failure. Deep learning models show promise for enhancing radiographic diagnosis. Objectives This review systematically evaluated the diagnostic performance of deep learning models in detecting peri‐implant marginal bone loss ...
Momen A. Atieh, Maanas Shah, Abeer Hakam, Fawaghi AlAli, Samhar AlSayed, Andrew Tawse‐Smith, Nabeel H. M. Alsabeeha   +6 more
wiley   +1 more source

Textile and colour defect detection using deep learning methods

Coloration Technology, EarlyView.
Abstract Recent advances in deep learning (DL) have significantly enhanced the detection of textile and colour defects. This review focuses specifically on the application of DL‐based methods for defect detection in textile and coloration processes, with an emphasis on object detection and related computer vision (CV) tasks.
Hao Cui, Abdel‐Fattah Seyam, Renzo Shamey   +2 more
wiley   +1 more source

CES-YOLOv8: Strawberry Maturity Detection Based on the Improved YOLOv8

Agronomy
Automatic harvesting robots are crucial for enhancing agricultural productivity, and precise fruit maturity detection is a fundamental and core technology for efficient and accurate harvesting. Strawberries are distributed irregularly, and their images contain a wealth of characteristic information.
Yongkuai Chen, Haobin Xu, Pengyan Chang, Yuyan Huang, Fenglin Zhong, Qi Jia, Lingxiao Chen, Huaiqin Zhong, Shuang Liu   +8 more
openaire   +2 more sources

OralSegNet: An Approach to Early Detection of Oral Disease Using Transfer Learning

Oral Diseases, EarlyView.
ABSTRACT Objective Deep learning‐based segmentation system is proposed that exploits three variants of YOLOv11 architecture, namely YOLOv11n‐seg, YOLOv11s‐seg, and YOLOv11m‐seg for automated detection and localization of the oral disease conditions from photographic intraoral images.
Pranta Barua, Md Rakibul Islam, Mahmud Uz Zaman, Adel Alenazi, Fawaz Alqahtani, Huda Abutayyem, Maher Al Shayeb, Ruba Odeh, Nesrine A. Elsahn, Mohammad Khursheed Alam   +9 more
wiley   +1 more source

UAVAI-YOLO：无人机航拍图像的小目标检测模型

智能科学与技术学报
针对无人机航拍图像目标检测效果差的问题，提出改进的UAVAI-YOLO模型。首先，为使模型获得更加丰富的语义信息，使用改进可变形卷积网络（deformable convolutional networks，DCN）替换原骨干（backbone）网络部分通道到像素（channel-to-pixel，C2f）模块原始卷积。其次，为增加P2特征层而不增加模型参数量，提出Conv_C模块将骨干网络输出通道降维，同时避免通道降维导致的语义信息丢失，使用改进ODConv卷积替换颈部（neck）部分C2f模块原始卷积。
何植仟, 曹立杰
doaj   +1 more source

Comparative performance of next-Gen YOLO models for leaf health classification in ornamental species [PDF]

BIO Web of Conferences
Automated plant disease detection has become an essential application of deep learning, supporting early diagnosis and effective crops and ornamental plant management. Recent advancements in the You Only Look Once (YOLO) family of object detection models
Chowdhuri Swati, Banerjee Sriparna, Mondal Tiyasha, Som Sayan, Debnath Shuvankar   +4 more
doaj   +1 more source

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

Object Detection Based on You Look Only Once Version 8 for Real-Time Applications

IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This research focus to involves human detection in crowded situations, especially in the lecturer's room. The lecturer's room is very vulnerable because it can be accessed by anyone with only one entry and exit to the lecturer's room, so it would be ...
Gede Agus Santiago, Putu Sugiartawan, Ni Nengah Dita Ardriani   +2 more
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