Results 41 to 50 of about 4,232 (214)

YOLOv5-AC: Attention Mechanism-Based Lightweight YOLOv5 for Track Pedestrian Detection

Sensors, 2022
In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non ...
Haohui Lv, Hanbing Yan, Keyang Liu, Zhenwu Zhou, Junjie Jing   +4 more
openaire   +3 more sources

Detection of Chrysanthemums Inflorescence Based on Improved CR-YOLOv5s Algorithm

, 2023
Accurate recognition of the flowering stage is a prerequisite for flower yield estimation. In order to improve the recognition accuracy based on the complex image background, such as flowers partially covered by leaves and flowers with insignificant ...
Xinyu Zheng, Dasheng Wu, Wentao Zhao
core   +1 more source

Modulation Recognition of Frequency Hopping Signal under Interference Based on Improved YOLOv5s [PDF]

Zhengzhou Daxue xuebao. Gongxue ban
In complex electromagnetic environments, interference signals can severely degrade the detection and recognition performance of frequency-hopping signals. To address issues of false detection, missed detection, and over-detection in traditional methods,
ZHANG Haibin, WEI Hongji, WANG Chao, XIANG Changbo, YANG Mingyang, LI Xiaolong   +5 more
doaj   +1 more source

Passable Area Evaluation of Tractor Road Based on Improved YOLOv5s and Multi-Factor Fusion

Agriculture
The tractor road, as the core scene for autonomous driving of grain transport vehicles, is unstructured, complex, and obstacle-rich, leading to poor real-time performance and accuracy of joint road and obstacle detection with existing YOLOv5s ...
Qian Zhang, Wenjie Xu, Wenfei Wu, Lizhang Xu, Zhenghui Zhao, Shaowei Liang   +5 more
doaj   +1 more source

DDVC-YOLOv5: An Improved YOLOv5 Model for Road Defect Detection

IEEE Access
Road defect detection is crucial for enhancing traffic safety, optimizing urban management efficiency, and promoting sustainable urban development. Traditional manual detection methods are inefficient and costly, and most deep learning-based road defect detection models lack superior feature extraction capabilities in complex environments.
Shihao Zhong, Chunlin Chen, Wensheng Luo, Siyuan Chen   +3 more
openaire   +2 more sources

Swin-YOLOv5: Research and Application of Fire and Smoke Detection Algorithm Based on YOLOv5

Computational Intelligence and Neuroscience, 2022
Accurate monitoring of fire and smoke plays an irreplaceable role in preventing fires and safeguarding the safety of citizens' lives and property. The network structure of YOLOv5 is simple, but using convolution to extract features will lead to some problems such as limited receptive field, poor feature extraction ability, and insufficient feature ...
Shangjie Ge Zhang, Fengxi Zhang, Yuyang Ding, Yu Li   +3 more
openaire   +2 more sources

YOLOv5s-CAM: A Deep Learning Model for Automated Detection and Classification for Types of Intracranial Hematoma in CT Images [PDF]

, 2023
Intracranial hematoma due to traumatic brain injury is a serious health concern with rates of morbidity and mortality that are increasing worldwide. Manual identification is slow, subject to observer variabilities, and the existing automated techniques ...
Mallappa, Sankalp, Barua, Prabal Datta, Basak, Sudipta, Raghavendra, U., Menon, Girish R., Menon, Girish R., Vidhya, V., Barua, Prabal Datta, Molinari, Filippo, Salvi, Massimo, Molinari, Filippo, Ciaccio, Edward J., Hegde, Ajay, Ciaccio, Edward J., Acharya, U. Rajendra, Salvi, Massimo, Gudigar, Anjan   +16 more
core   +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

YOLOv5s-gnConv: detecting personal protective equipment for workers at height [PDF]

, 2023
IntroductionFalls from height (FFH) accidents can devastate families and individuals. Currently, the best way to prevent falls from heights is to wear personal protective equipment (PPE).
Huanxi Wen, Huihua Chen, Yaoyu Li, Xiaodong Hu   +3 more
core   +1 more source

HDS-YOLOv5: An improved safety harness hook detection algorithm based on YOLOv5s

Mathematical Biosciences and Engineering, 2023
<abstract> <p>Improperly using safety harness hooks is a major factor of safety hazards during power maintenance operation. The machine vision-based traditional detection methods have low accuracy and limited real-time effectiveness. In order to quickly discern the status of hooks and reduce safety incidents in the complicated operation ...
Mingju Chen, Zhongxiao Lan, Zhengxu Duan, Sihang Yi, Qin Su   +4 more
openaire   +3 more sources