Results 131 to 140 of about 17,375 (236)

System Integration and Field Testing of Low‐Cost Autonomous Platooning Platforms: Collision Avoidance Scenario

Journal of Field Robotics, Volume 43, Issue 2, Page 847-878, March 2026.
ABSTRACT This paper presents the development and validation of a scalable platooning system based on the predecessor‐following (PF) topology, designed for low‐cost follower platforms. It integrates key technologies such as localization, path planning, profile generation, and low‐level control to create a practical solution.
Dongwoo Seo, Jinhee Lee, Jaeyoung Kang
wiley   +1 more source

A lightweight YOLO-TinyFuse model for small target detection of olive fruits. [PDF]

Front Plant Sci
Yang X, Lin Y, Xiao Q, Liang Z, Ma L, ShuoGuo Y, Ade K, Tong Z, Chen Y, Cao Y.   +9 more
europepmc   +1 more source

Smart Waste, Smarter World: Exploring Waste Types, Trends, and Tech‐Driven Valorization Through Artificial Intelligence, Internet of Things, and Blockchain

Sustainable Development, Volume 34, Issue S2, Page 132-153, March 2026.
ABSTRACT Global municipal solid waste generation is projected to exceed 3.8 billion tonnes annually by 2050. This makes the need for smart, inclusive, and scalable waste valorization systems more urgent than ever. This review critically explores the shift from conventional waste management to intelligent, technology‐driven solutions aligned with ...
Segun E. Ibitoye, Amit K. Ball, Raul V. Rodriguez, Olalekan A. Olayemi, Peplluis Esteva, Peter O. Omoniyi, Isaac K. Adegun, Rasheedat M. Mahamood, Esther T. Akinlabi   +8 more
wiley   +1 more source

DMS-YOLO: Small target detection algorithm based on YOLOv11. [PDF]

PLoS One
Huang M, Jiang W.
europepmc   +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

Effect of Boulder‐Size Distributions on Thermally Derived Rock Abundances on the Moon

Journal of Geophysical Research: Planets, Volume 131, Issue 3, March 2026.
Abstract Rock abundance, defined as the surface's fractional area covered by rocks, is used to characterize the Moon's regolith, decipher its impact history, and assess potential landing sites. By definition, it should be agnostic to rock‐size distributions. However, it has been suggested that rock abundances, derived from surface temperatures measured
Brian Amaro, Nils Prieur, Lior Rubanenko, Paul Hayne, Mathieu Lapôtre   +4 more
wiley   +1 more source

LE-YOLO: A Lightweight and Enhanced Algorithm for Detecting Surface Defects on Particleboard

BioResources
Current algorithms for surface defect detection in particleboard often encounter limitations such as high computational complexity and excessive parameter scale.
Chao He, Yongkang Kang, Anning Ding, Wei Jia, Huaqiong Duo   +4 more
doaj  

A general method for detection and segmentation of terrestrial arthropods in images

Methods in Ecology and Evolution, Volume 17, Issue 3, Page 727-739, March 2026.
Abstract Automated monitoring of insects and other arthropods is vital for ecological research and conservation; yet current image recognition tools often lack generalizability across diverse imaging conditions and struggle with varying specimen sizes. This limits their practical utility and wide adoption by ecologists.
Asger Svenning, Guillaume Mougeot, Jamie Alison, Daphne Chevalier, Nisa Chavez Molina, Song‐Quan Ong, Kim Bjerge, Juli Carrillo, Toke Thomas Høye, Quentin Geissmann   +9 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, Volume 174, Issue 3, Page 212-224, March 2026.
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

POLD-YOLO: A Lightweight YOLO11-Based Algorithm for Insulator Defect Detection in UAV Aerial Images. [PDF]

Sensors (Basel)
Hu B, Wu F, Zhang P, Cui J, Liu Y.
europepmc   +1 more source