Results 81 to 90 of about 17,375 (236)

Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a UAV‐based framework that integrates deep learning‐based super‐resolution reconstruction and an enhanced YOLO detector to improve centimetre‐scale benthic organism monitoring. Using hermit crabs in Lake Hamana, a coastal lagoon in Japan, as a case study, the method substantially enhanced small‐object detection performance ...
Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno   +10 more
wiley   +1 more source

Time‐series digital camera photos combined with machine learning algorithms can realize accurate observation of flowering phenology

Remote Sensing in Ecology and Conservation, EarlyView.
Intelligent approaches are required to extract valuable phenological information from time‐series digital camera photos. In this research, we employed YOLO‐based object detection and semantic segmentation models to identify flowers and flower pixels, acquire flower count and flower cover data, and extract phenophases such as first, peak, and end ...
Chuangye Song, Yuan Jia, Lin Zhang, Dongxiu Wu   +3 more
wiley   +1 more source

Computer vision for Pokémon Battles: A YOLO and Tesseract-Based System for Automated Recognition and Gameplay Analysis

Interfases
Pokémon Double Battles present a complex decision-making environment that has traditionally relied on manual data analysis. This paper introduces an automated system leveraging computer vision and deep learning to extract structured gameplay data from ...
Miguel R. Lladó, Terence Morley
doaj   +1 more source

The performance of drones and artificial intelligence for monitoring sage‐grouse at leks

Wildlife Biology, EarlyView.
Accurately monitoring sage‐grouse populations is critical for conservation, yet traditional ground‐based visual surveys face challenges in scalability and consistency, prompting the exploration of innovative drone‐based methodologies enhanced by artificial intelligence.
Lance B. McNew, Jason Hanlon, Ilya Buzytsky   +2 more
wiley   +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

Real-Time Pose Estimation of Preterm Infants Using Depth Images

Current Directions in Biomedical Engineering
Early diagnosis of neurodevelopmental disorders in infants relies on accurate analysis of spontaneous movements. Achieving this requires fast and precise pose estimation methods tailored to infant-specific anatomy and motion. This study evaluates several
Vogelsang Tobias, Fahlbusch Fabian B., Behr Anna-Lena, Zaunseder Sebastian   +3 more
doaj   +1 more source

Dynamic Adaptive Label Assignment for Tiny Object Detection in Remote Sensing Images

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT With the development of unmanned aerial vehicle and satellite technology, the application of tiny object detection in remote sensing images is becoming increasingly widespread. Although significant progress has been made in the accuracy and speed of object detection in recent years, performance declines sharply when general object detectors ...
Shuohao Shi, Qiang Fang, Xin Xu
wiley   +1 more source

GY-YOLO: ghost separable YOLO for pedestrian detection

Neural Computing and Applications
Abstract In recent years, there has been impressive development in human detection. The main challenge in pedestrian detection is the training data. To assess detectors in crowd scenarios more effectively, a novel dataset in this study called the HEP dataset (Hybrid Egyptian Pedestrian dataset) is introduced. The HEP dataset is extensive, has
Ali M. Elhenidy, Labib M. Labib, Amira Y. Haikal, Mahmoud M. Saafan   +3 more
openaire   +1 more source

FD-YOLO: A YOLO Network Optimized for Fall Detection

Applied Sciences
Falls are defined by the World Health Organization (WHO) as incidents in which an individual unintentionally falls to the ground or a lower level. Falls represent a serious public health issue, ranking as the second leading cause of death from unintentional injuries, following traffic accidents.
Hoseong Hwang, Donghyun Kim, Hochul Kim
openaire   +2 more sources

MSFFNet: Multiscale Feature Fusion Network for Small Target Detection in Remote Sensing Images

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT With the advancement of satellite remote sensing technology, object detection based on high‐resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision. Although numerous algorithms have been developed for remote sensing image object detection, they still suffer from challenges such as low ...
Hui Zong, Chenbin Wang, Hao Wang, Xin Cheng, Shangbing Gao, Yunyang Yan   +5 more
wiley   +1 more source