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!
Description
Summary: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 accuracy. We introduce a new lightweight convolutional technique, GSConv, to lighten the model but maintain the accuracy. The GSConv accomplishes an excellent trade-off between the accuracy and speed. Furthermore, we provide a design suggestion based on the GSConv, slim-neck (SNs), to achieve a higher computational cost-effectiveness of the real-time detectors. The effectiveness of the SNs was robustly demonstrated in over twenty sets comparative experiments. In particular, the real-time detectors of ameliorated by the SNs obtain the state-of-the-art (70.9% AP 50 for the SODA10M at a speed of ~ 100 FPS on a Tesla T4) compared with the baselines. Code is available at https://github.com/alanli1997/slim-neck-by-gsconv .
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1861-8200
1861-8219
DOI:10.1007/s11554-024-01436-6