Results 81 to 90 of about 7,696 (200)

Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity

Clinical and Experimental Dental Research, Volume 12, Issue 3, June 2026.
ABSTRACT Background AI‐based dentistry has advanced significantly in recent years. AI models like deep learning (DL) and machine learning (ML) have paved the way for new approaches to image diagnostics and early risk prediction, making patient treatment plans more personalized. Aim The objective of this study was to assess the explainability, bias, and
Vini Mehta, Ankita Mathur, Mahati Bhadania, Cosimo Galletti, Javier Flores‐Fraile   +4 more
wiley   +1 more source

Drone-based solar panel inspection using machine learning [PDF]

EPJ Web of Conferences
The paper represents the experimental implementation of a Drone-based solar panel inspection using YOLOv8-based object detection with Ultraviolet sensing for automated and enhanced defect inspection.
Praburam Jiten, Afzel Mohammed, Krishnamurthy Balachandar   +2 more
doaj   +1 more source

Basketball detection based on YOLOv8

PLOS One
Accurate and timely detection of basketballs is crucial for ensuring fairness in games, enhancing the precision of data analysis, optimizing tactical planning for coaches, and improving the spectator experience. However, current basketball detection technologies face challenges such as variations in target scale, scene complexity, and changing camera ...
Zeyu Liang, Jiuyuan Wang, Tianhao Huang, Zilong Sang, Jia Zhang   +4 more
openaire   +2 more sources

DualPath‐DRNet: A Self‐Annotating Dual‐Path Networks for End‐To‐End Diabetic Retinopathy Diagnosis

Engineering Reports, Volume 8, Issue 6, June 2026.
DualPath‐DRNet for automated diabetic retinopathy diagnosis and grading. ABSTRACT One of the challenges in detecting Diabetic Retinopathy (DR) is the detection of subtle early‐stage microaneurysms. DualPath‐DRNet is an end‐to‐end deep learning pipeline for 5‐class DR prediction. The novelty of this method lies in combining lesion detection using YOLOv8,
Ankur Chaudhary, Neetu Faujdar, Ghanshyam Singh   +2 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

BSMD-YOLOv8: Enhancing YOLOv8 for Book Signature Marks Detection

Applied Sciences
In the field of bookbinding, accurately and efficiently detecting signature sequences during the binding process is crucial for enhancing quality, improving production efficiency, and advancing industrial automation. Despite significant advancements in object detection technology, verifying the correctness of signature sequences remains challenging due
Long Guo, Lubin Wang, Qiang Yu, Xiaolan Xie   +3 more
openaire   +2 more sources

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

YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection

Applied Sciences
During the developmental stages, eggplants are susceptible to diseases, which can impact crop yields and farmers’ economic returns. Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. However, eggplant
Yuxi Huang, Hong Zhao, Jie Wang
openaire   +2 more sources

AI‐Enabled Mucus Segmentation in Nasal Endoscopy with State Space and Attention‐Based Modeling

Laryngoscope Investigative Otolaryngology, Volume 11, Issue 3, June 2026.
We developed SUM‐MucusNet, an artificial intelligence system for reliable mucus segmentation in nasal endoscopy, designed to overcome challenges from poor image quality and illumination artifacts. The model represents a modest but statistically supported improvement over the next‐best model, achieving a Dice score of 71%. SUM‐MucusNet enables real‐time
Dipesh Gyawali, Jonathan Bidwell, Elena Karras, Thomas J. Mundy, Kayla E. Baker, Elena D. Bartolone, Dhara Patel, Akio Fujiwara, Edward D. McCoul   +8 more
wiley   +1 more source

Augmentation for Accuracy Improvement of YOLOv8 in Blind Navigation System

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This study addresses the critical need for enhanced accuracy in YOLOv8 models designed for visually impaired navigation systems. Existing models often struggle with consistency in object detection and distance estimation under varying environmental ...
Erwin Syahrudin, Ema Utami, Anggit Dwi Hartanto   +2 more
doaj   +1 more source