Results 71 to 80 of about 17,190 (230)

Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research [PDF]

, 2023
Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high ...
Carlsen, Astrid, Hanes, Matilda, Hentati-Sundberg, Jonas, Kad, Shreyash, Kasarareni, Siddharta, Olin, Agnes, Olsson, Olof, Reddy, Mareddy, Reddy, Sheetal   +8 more
core  

AI‐assisted automated endpoint interpretation for turbidity‐based digital LAMP using bright‐field microscopy

VIEW, EarlyView.
Turbidity‐based digital LAMP offers a low‐cost alternative for nucleic acid quantification, but interpreting bright‐field images is difficult due to weak contrast and noise. We developed NanoFuse‐YOLO11, an AI‐based detection model that automatically identifies positive reaction units in turbidity microscopy images.
Zhu Chen, Yiliang Bao, Yue Wang, Kaixuan Guo, Meimei Zeng, Yan Deng, Song Li, Yuan Guo   +7 more
wiley   +1 more source

PQD recognition using two-dimensional time-frequency spectrograms and an improved YOLOv5

Zhejiang dianli
As the penetration rate of renewable energy sources increases in new-type power systems， so too does the complexity of the grid structure， leading to more diverse and complex power quality disturbance （PQD）. To accurately identify power quality， a method
LI Xin, LYU Ganyun, GONG Yu, BI Ruihua, YE Jiaxing, LIU Xiaohong, YU Xiangyi   +6 more
doaj   +1 more source

Towards Fully Autonomous UAV:Damaged Building-Opening Detection for Outdoor-Indoor Transition in Urban Search and Rescue [PDF]

Autonomous unmanned aerial vehicle (UAV) technology is a promising technology for minimizing human involvement in dangerous activities like urban search and rescue missions (USAR), both in indoor and outdoor.
Bergado, John Ray, Nex, Francesco, Surojaya, Ali, Zhang, Ning   +3 more
core   +1 more source

Evaluating machine learning models for multi‐species wildlife detection and identification on remote sensed nadir imagery in South African savanna

Wildlife Biology, EarlyView.
This research paper investigates the efficacy of leading machine learning (ML) models for detecting and identifying ungulate species in African savanna using nadir imagery from unmanned aerial vehicles (UAVs). Traditional aerial counting methods, while widely used, suffer from significant limitations in accuracy and precision, in part due to human ...
Paul Allin, Fadel Seydou, Radloff Frans, Andrew Davies, Alison Leslie   +4 more
wiley   +1 more source

เครื่องจ่ายยาอัตโนมัติเเละตรวจสอบความถูกต้องด้วยโมเดล YOLOv5

Journal of Computer and Creative Technology
วารสารคอมพิวเตอร์และเทคโนโลยีสร้างสรรค์, 2, 2, 45 ...
openaire   +2 more sources

Intelligent monitoring method for conveyor belt misalignment based on deep learning

Gong-kuang zidonghua
Existing methods for monitoring conveyor belt misalignment face challenges in terms of practicality, robustness, and the difficulty of dataset creation.
ZUO Mingming, ZHANG Xi, YANG Zihao, SUN Qifei, ZHANG Mengchao, ZHANG Yuan, LI Hu   +6 more
doaj   +1 more source

Deep Learning Models for Dental Conditions Classification Using Intraoral Images [PDF]

This paper presents the digitalization of dentistry medical records to support the dentist in the patient examination process. A dentist uses manual input to fill out the evaluation form by drawing and labeling each patient’s tooth condition based on ...
Bayu Dewantara, Bima Sena, Brahmanta, Arya, Karlita, Tita, Makarim, Ahmad Fauzi, Sigit, Riyanto   +4 more
core   +2 more sources

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

Yolov5s-PSG: Improved Yolov5s-Based Helmet Recognition in Complex Scenes

IEEE Access
In the field of industrial safety, due to the existence of color, distance and other reasons in complex industrial environments caused by the helmet small target detection methods have the problem of misdetection and omission, and the Yolov5s model for real-time detection of helmets is not ideal.
Yi Li, Xiaolin Shi, Xiaolong Xu, Han Zhang, Fan Yang   +4 more
openaire   +2 more sources