Results 61 to 70 of about 12,664 (249)

Object Detection using YOLOv8 : A Systematic Review

Sistemasi: Jurnal Sistem Informasi
This study is a Systematic Literature Review (SLR) that comprehensively reviews the recent advances in YOLOv8-based object detection models and their implementations in various application fields, such as UAV aerial photography, fruit ripeness ...
Nugraha Asthra Megantara, Ema Utami
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

Design, Training, and Field Validation of a YOLOv8‐Based Automated Trap for Remote Detection of Codling Moth, Cydia pomonella

Entomologia Experimentalis et Applicata, EarlyView.
We developed and field‐validated a fully automated trap for Cydia pomonella monitoring, combining a camera with a YOLOv8 object‐detection model for remote insect identification. In controlled laboratory validation, the model showed strong performance (precision 0.77, recall 0.83), with moderate reductions under field conditions. Across six experiments,
Veronica Carnio, Michele Preti, Riccardo Favaro, Orneda Lecini, Giovanni Giannotta, Sergio Angeli   +5 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

Lightweight coal particle group instance segmentation method based on YOLOv8

Meitan xuebao
In the foundational research field of mine gas control, a large quantity of coal particles is widely employed in various experiments. Due to the significant impact of particle size distribution on experimental results, the precise and rapid determination
Jingjing LIU, Fengqian SUN, Haoxiang ZHANG, He JIANG, Junhui CHEN, Qiqi KOU, Deqiang CHENG   +6 more
doaj   +1 more source

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

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

Foreign object detection of coal mine conveyor belt based on improved YOLOv8

Gong-kuang zidonghua
The existing deep learning based foreign object detection models for conveyor belts are relatively large and difficult to deploy on edge devices. There are errors and omissions in detecting foreign objects of different sizes and small objects.
HONG Yan, WANG Lei, SU Jingming, WANG Hantao, LI Mushi   +4 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

Research on parking detection algorithm based on yolov8 [PDF]

, 2023
Zhijia LI, Haiping Wei
openalex   +1 more source