Results 91 to 100 of about 12,214 (239)

Comparative analysis of YOLOv5 and MobileNetV3 models for real-time image recognition

Вісник Харківського національного університету імені В.Н. Каразіна. Серія: Математичне моделювання, інформаційні технології, автоматизовані системи управління
Relevance: With the growing need for fast and accurate real-time object recognition, especially for mobile and embedded systems, the question of choosing the optimal AI models arises.
Ярослав Ясінський, Ніна Бакуменко   +1 more
doaj   +1 more source

YOLO‐GDCNN: Real‐Time Operating Point Detection for Live Working Robots in the Power Industry

High Voltage, EarlyView.
ABSTRACT In the power industry maintenance, the capability of live working robots to detect and operate with power components in real time is paramount. This paper proposes a cascaded detection framework for real‐time detection of live working operation points, named YOLO‐GDCNN. The framework consists of two parts.
Haoning Zhao, Jiamin Guo, Erbao Dong, Rui Guo, Linkun Zhao, Chaoqun Wang, Xuewen Rong, Yibin Li   +7 more
wiley   +1 more source

Large coal detection for belt conveyors based on improved YOLOv5

Gong-kuang zidonghua
Oversized coal blocks can easily cause poor coal flow, blockage, and coal stacking when transported on a belt conveyor. However, the differences in appearance and color between large coal blocks and ordinary coal blocks are small, and there are ...
QIN Yulong, CHENG Jiming, REN Yige, WANG Xiaoqing, ZHAO Qing, AN Cuijuan   +5 more
doaj   +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

Deep Learning Models for Detection of Periapical Radiolucent Lesions on Panoramic Radiographs: A Systematic Review and Meta‐Analysis

International Endodontic Journal, EarlyView.
ABSTRACT Background Panoramic radiographs are used routinely to screen dental conditions and treatment patterns. Recently, numerous studies have suggested that deep learning (DL) models can be utilized for analysing panoramic radiographs. Objective This review aimed to evaluate the accuracy of DL models in detecting periapical radiolucent lesions (PRLs)
Ibrahim Ali Ahmad, Raidan Ba‐Hattab, Sadeq Ali Al‐Maweri, Faleh Tamimi   +3 more
wiley   +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

YOLOv5s-BC: an improved YOLOv5s-based method for real-time apple detection

Journal of Real-Time Image Processing
To address the issues associated with the existing algorithms for the current apple detection, this study proposes an improved YOLOv5s-based method, named YOLOv5s-BC, for real-time apple detection, in which a series of modifications have been introduced.
Jingfan Liu, Zhaobing Liu
openaire   +2 more sources

Improved Efficiency and Lesion Detection in Small Bowel Capsule Endoscopy Using the Open‐Source Artificial Intelligence Model SEE‐AI

DEN Open, Volume 7, Issue 1, April 2027.
AI‐assisted reading with the open‐source SEE‐AI model improves lesion detection sensitivity and reduces reading time in small‐bowel capsule endoscopy. In a multicenter retrospective study of 249 cases, SEE‐AI provides visual cues that support physicians during interpretation while preserving physician‐led final decisions.
Satoshi Miyazono, Junji Umeno, Tomohiro Nagasue, Takuto Saiki, Hisamitsu Kaku, Takehiro Torisu, Akihito Yokote, Keisuke Kawasaki, Yutaro Ihara, Yuichi Matsuno, Noriyuki Imazu, Tomohiko Moriyama, Ahmed Nashaat Mohamed, Katsuya Hirakawa, Hajime Yamagata, Yasuharu Okamoto, Koichi Kurahara, Shinichiro Yada, Akira Harada, Tetsuro Ago   +19 more
wiley   +1 more source

Pixel‐level supervision resolves overlap: Benchmarking YOLOv12 segmentation for accurate multi‐cluster dry bean stand counting from time series unoccupied aerial systems imagery

The Plant Phenome Journal, Volume 9, Issue 1, December 2026.
Abstract An agronomic trait such as stand count is important for cultivar development and crop management practices. Manually counting the number of plants is time consuming, labor‐intensive, and prone to error. The use of unoccupied aerial systems (UAS)‐collected red, green, blue (RGB) imagery in conjunction with advanced deep learning and image ...
Aliasghar Bazrafkan, Valerio Hoyos‐Villegas   +1 more
wiley   +1 more source

Detection method of heterotropic fiber based on improved YOLOv5

, 2022
Aiming at the problems of inaccuracy and poor real-time detection of heterosexual fiber in cotton cleaning process, a target detection model of heterosexual fiber based on YOLOv5 network was proposed to realize fast and accurate identification and ...
Yuhong Du, Hengli Zuo
core   +1 more source