Slim-neck by GSConv: a lightweight-design for real-time detector architectures

Real-time object detection is significant for industrial and research fields. On edge devices, a giant model is difficult to achieve the real-time detecting requirement, and a lightweight model built from a large number of the depth-wise separable convolutional could not achieve the sufficient accur...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of real-time image processing Vol. 21; no. 3; p. 62
Main Authors:	Li, Hulin, Li, Jun, Wei, Hanbing, Liu, Zheng, Zhan, Zhenfei, Ren, Qiliang
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2024 Springer Nature B.V
Subjects:	Accuracy Computational efficiency Computer Graphics Computer Science Design Detectors Effectiveness Image Processing and Computer Vision Multimedia Information Systems Neural networks Object recognition Pattern Recognition Real time Sensors Signal,Image and Speech Processing Deep learning GSConv Real-time detection Lightweight CNNs Edge computing
ISSN:	1861-8200, 1861-8219
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!