Results 121 to 130 of about 5,295 (214)

Lightweight ship detection method based on YOLO-FNC model

Zhongguo Jianchuan Yanjiu
ObjectiveA lightweight and efficient ship detection method based on the YOLO-FNC model is proposed for complex environments such as ports with dense traffic. MethodFirst, a FasterNeXt neural network module is designed on the basis of the FasterNet method
Bingyan ZHANG, Chuang ZHANG, Zhennan SHI, Songtao LIU   +3 more
doaj   +1 more source

SHINE: Deep Learning-Based Accessible Parking Management System

, 2023
The ongoing expansion of urban areas facilitated by advancements in science and technology has resulted in a considerable increase in the number of privately owned vehicles worldwide, including in South Korea.
Aryal, Sunil, Bhattarai, Aashish, Bouadjenek, Mohamed Reda, Neupane, Dhiraj, Seok, Jongwon, Seok, Uk-Min   +5 more
core  

Performance Evaluation of Real-Time Object Detection for Electric Scooters

Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise of e-scooters has been marked by a concerning increase in related injuries
Campbell, Bradford, Chen, Dong, Heydarian, Arsalan, Hosseini, Arman, Nikkhah, Amir Farzin, Shoghli, Omid, Smith, Arik   +6 more
core  

Fe-Yolov5: Feature Enhancement Network Based on Yolov5 for Small Object Detection

SSRN Electronic Journal, 2022
Min Wang, Wenzhong Yang, Liejun Wang, Danny Chen, Fuyuan Wei, HaiLaTi KeZiErBieKe, Yuanyuan Liao   +6 more
openaire   +1 more source

Real-time guidewire tracking and segmentation in intraoperative x-ray

During endovascular interventions, physicians have to perform accurate and immediate operations based on the available real-time information, such as the shape and position of guidewires observed on the fluoroscopic images, haptic information and the ...
Bourier, Felix, Bui, Mai, Navab, Nassir, Schunkert, Heribert, Wang, Cheng, Zhang, Baochang   +5 more
core   +1 more source

Research on the Detection Method of Safflower Filaments in Natural Environment Based on Improved YOLOv5s

IEEE Access
The accurate and rapid identification of safflower filaments is a prerequisite for automating harvesting. This paper proposes a lightweight, high-precision detection model for safflower filaments based on YOLOv5s, named YOLOv5s-MCD, to address the issues
Bangbang Chen, Feng Ding, Baojian Ma, Xiangdong Liu, Shanping Ning   +4 more
doaj   +1 more source

A novel Multi to Single Module for small object detection

, 2023
Small object detection presents a significant challenge in computer vision and object detection. The performance of small object detectors is often compromised by a lack of pixels and less significant features.
Guo, Xiaohui
core  

ECTR-YOLOv5:Pedestrian detection in dense scenes based on improved YOLOv5

Abstract Pedestrian detection technology has reached a relatively mature level in sparse environments. However, accurate pedestrian detection in packed scenes still presents challenges owing to factors such as occlusion, high crowd density, and scale changes.
yiheng wu, laichun li, taihui liu, wei meng, chenwei wu   +4 more
openaire   +1 more source

IDENTIFICATION OF COLORADO BEETLES USING YOLOV5 [PDF]

The monitoring of Colorado potato beetles in large fields is a complex and time-consuming process that requires accurate data collection, analysis, and interpretation.
Dembovskis, Jānis, Kodors, Sergejs, Rudzusieks, Kaspars   +2 more
core   +3 more sources

R‐YOLOv5s: Improved YOLOv5s for Object Detection in Low‐Light Environments

International Journal of Intelligent Systems
In response to the challenge of low detection accuracy exhibited by mainstream object detection models in low‐light environments, this paper proposes a novel detection model named R‐YOLOv5s. The model incorporates several key enhancements to address this issue.
Yimeng Xia, Yuanmei Wang, Hao Luo, Shengzhe Liu, Tao Li   +4 more
openaire   +1 more source