Results 71 to 80 of about 12,214 (239)

YOLOv5-DCN: An Effective Improvement Based on YOLOv5 Detector

International Journal of Computer Science and Information Technology
Urine sediment detection is of great significance for the diagnosis and monitoring of kidney diseases, urinary tract infections, stones, etc. This study aims to propose fast, high-precision and lightweight Urinary particles detection model based on YOLOv5, deformable convolution, and evaluate its performance in Urinary particles detection tasks.
Xiaoxia Qi, Md Gapar Md Johar, Ali Khatibi, Jacquline Tham, Long Cheng   +4 more
openaire   +1 more source

Automated Lawn Maintenance: An Agronomic and Operational Review of Turf Health, Biodiversity, and Field Performance

Journal of Field Robotics, EarlyView.
ABSTRACT Grass mowing is one of the most resource‐consuming activities in green maintenance, whether in private areas such as home gardens or in public spaces like urban parks. In recent years, concerns related to climate change, human health, and sustainability have become increasingly prominent in green maintenance, leading manufacturers and industry
Andrea Palladini, Lorenzo Guerrini, Marco Fontanelli, Lucia Bortolini   +3 more
wiley   +1 more source

Helmet detection method based on improved YOLOv5

工程科学学报
To address the challenge of low detection accuracy in existing safety helmet detection algorithms, particularly in scenarios with small targets, dense environments, and complex surroundings like construction sites, tunnels, and coal mines, we introduce ...
Gongyu HOU, Qinhuang CHEN, Zhenhua YANG, Youwen ZHANG, Danyang ZHANG, Haoxiang LI   +5 more
doaj   +1 more source

Yield Estimation Method of Apple Tree Based on Improved Lightweight YOLOv5

智慧农业, 2021
Yield estimation of fruit tree is one of the important works in orchard management. In order to improve the accuracy of in-situ yield estimation of apple trees in orchard, a method for the yield estimation of single apple tree, which includes an improved
LI Zhijun, YANG Shenghui, SHI Deshuai, LIU Xingxing, ZHENG Yongjun   +4 more
doaj   +1 more source

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

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

Detecting Plateau Zokor (Eospalax baileyi) Mounds in UAV Imagery of Alpine Meadows Using Deep Learning Algorithms

Remote Sensing in Ecology and Conservation, EarlyView.
We developed PZM‐YOLO to automatically detect plateau zokor mounds in UAV imagery of alpine meadows. The model achieved reliable detection of small and densely distributed mounds under complex backgrounds, outperforming the baseline YOLOv5s. This framework supports mound counting, mound position, rodent impact assessment, and grassland restoration ...
Yang Yang, Limin Hua, Lianguo Wang, Jiazhen Li, Rui Dong, Siwei Yang   +5 more
wiley   +1 more source

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

Fusion or Confusion? Assessing the Impact of Visible‐Thermal Image Fusion for Automated Wildlife Detection

Remote Sensing in Ecology and Conservation, EarlyView.
This study evaluates the performances of synchronous aerial visible (VIS) and thermal infrared (TIR) imagery for detecting great blue heron (Ardea herodias) nests and individuals using a YOLO11n model. VIS and TIR images were automatically aligned using deep learning, and both early and late fusion approaches were tested.
Camille Dionne‐Pierre, Samuel Foucher, Jérôme Théau, Jérôme Lemaître, Patrick Charbonneau, Maxime Brousseau, Mathieu Varin   +6 more
wiley   +1 more source

YOLOv5 garbage classification method with GhostNet

Dianzi Jishu Yingyong
Garbage classification is an important part of building ecological civilization. To solve the problem that heavyweight models are difficult to deploy to mobile devices, an improved garbage image classification method based on YOLOv5 network is proposed ...
Li Yao, Hu Junguo, Le Yang
doaj   +1 more source