Results 101 to 110 of about 10,507 (209)

Real-Time Plant Leaf Counting Using Deep Object Detection Networks

Sensors, 2020
The use of deep neural networks (DNNs) in plant phenotyping has recently received considerable attention. By using DNNs, valuable insights into plant traits can be readily achieved.
Michael Buzzy, Vaishnavi Thesma, Mohammadreza Davoodi, Javad Mohammadpour Velni   +3 more
doaj   +1 more source

Artificial Intelligence–Driven Colonoscopy: A Systematic Review and Network Meta‐Analysis on System Performance for Colorectal Neoplasia Detection

JGH Open, Volume 10, Issue 3, March 2026.
ABSTRACT Background Colorectal cancer (CRC) is a major global health issue, with adenomatous polyps as the main precursors. Standard colonoscopy is the gold standard, but its effectiveness is limited by operator variability and missed lesions. Artificial Intelligence (AI)‐assisted colonoscopy is emerging to enhance detection, but a comparative ...
F. N. U. Eman, F. N. U. Gyaneshwari, Raveena Kumari, Afshan Jabeen, Karoona Kumari, F. N. U. Mansha, Shagufta Sattar, Fatima Zehra, Sonika Hotwani, Muhammad Talha Kakar, Hira Riaz   +10 more
wiley   +1 more source

AI‐Driven Deep Learning Framework for Detecting Subtle Surface Defects on Wind Turbine Blades

Wind Energy, Volume 29, Issue 3, March 2026.
ABSTRACT Wind turbine blade surface defect detection is of great significance in ensuring the safety and operational efficiency of wind power systems. However, accurately detecting subtle and small‐scale defects remains challenging under complex imaging conditions.
Shoutu Li, Jiawei Pan, Ling Liu, Jin Zhao, Nianxi Yue, Zhen Wu   +5 more
wiley   +1 more source

Improved YOLOv5 for Efficient Elimination of Unwanted Video Frames From High‐Speed Video Arrays Capturing Bat's Kinematics

Applied AI Letters, Volume 7, Issue 1, February 2026.
Deep learning pipeline developed to filter high‐speed bat flight recordings captured inside a custom flight tunnel. ABSTRACT This research aims to investigate the flying kinematics of bats, drawing inspiration from nature to benefit humans in achieving vision‐independent flights. The project uses 50 cameras mounted inside a specially designed tunnel to
D. P. Jayathunga, M. Ramashini, Juliana Zaini, R. Müller, Liyanage C. De Silva   +4 more
wiley   +1 more source

YOLOv3-A: a traffic sign detection network based on attention mechanism

Tongxin xuebao, 2021
To solve the problem that the existing YOLOv3 algorithm had more false detections and missed detections for traffic sign detection task with small target problems and complex background, based on the YOLOv3, a channel attention method for target ...
Fan GUO, Yongxiang ZHANG, Jin TANG, Weiqing LI   +3 more
doaj   +2 more sources

Multitarget Recognition of Flower Images Based on Lightweight Deep Neural Network and Transfer Learning

Advanced Intelligent Systems, Volume 8, Issue 2, February 2026.
This article proposes a lightweight YOLOv4‐based detection model using MobileNetV3 or CSPDarknet53_tiny, achieving 30+ FPS and higher mAP. It also presents a ShuffleNet‐based classification model with transfer learning and GAN‐augmented images, improving generalization and accuracy.
Qingyang Liu, Yanrong Hu, Hongjiu Liu
wiley   +1 more source

Bayer Marker Detection based on Yolov3

IOP Conference Series: Earth and Environmental Science, 2020
Abstract With the development of artificial intelligence technology, target detection is widely used in many commercial fields, such as automatic driving, security monitoring and robotics, especially the deep learning algorithm has brought about a significant improvement in target detection performance.
openaire   +1 more source

An improved method of Tiny YOLOV3

IOP Conference Series: Earth and Environmental Science, 2020
Abstract Target detection is the basic technology of self-driving system. In this paper, the problem of high detection rate of pedestrians and other small targets is studied in real-time detection of Tiny YOLOV3 target detection algorithm, and the network structure of Tiny YOLOV3 algorithm is improved.
Xiaotian Gong, Li Ma, Hangkong Ouyang
openaire   +1 more source

Comparison of YOLOv3-tiny and YOLOv4-tiny in the Implementation Handgun, Shotgun, and Rifle Detection Using Raspberry Pi 4B

Jurnal Elektronika dan Telekomunikasi
Criminal activities frequently involve carryable weapons such as handguns, shotguns, and rifle classes. Frequently, the targets of these weapons that are captured are concealed from plain sight by the people of the crowd.
Faris zulkarnain S. Hi. Rauf, Djati Handoko, Ilham S Pradana, Dimas Alifta   +3 more
doaj   +1 more source

Locating Sheep with YOLOv3

, 2019
De siste årene har ubemannede flyvende fartøy hatt en stor vekst innen mange forskjellige fagområder, fra inspeksjoner på strømnettet til overvåking av vilt. Et av disse feltene innebærer å lokalisere sau på beite, spesifikt i forbindelse med innsankning nær slutten av beiteperioden.
openaire   +1 more source