Gelan-SE: Squeeze and Stimulus Attention Based Target Detection Network for Gelan Architecture
In the field of computer vision, the development of efficient target detection models for resource-constrained environments represents a significant challenge. The objective of this study is to optimize and enhance the Generalized Efficient Layer Aggregation Network (GELAN) architecture of YOLOv9, t...
Saved in:
| Main Authors: | Lili Wang, Sukumar Letchmunan, Renhao Xiao |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10681406/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
LSEVGG: An attention mechanism and lightweight-improved VGG network for remote sensing landscape image classification
by: Yao Lu
Published: (2025-08-01) -
SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification
by: Shandong Yuan, et al.
Published: (2025-01-01) -
Spatial and Channel Attention Integration with Separable Squeeze-and-Excitation Networks for Image Classifications
by: Nazmul Shahadat, et al.
Published: (2025-05-01) -
Enhanced effective convolutional attention network with squeeze-and-excitation inception module for multi-label clinical document classification
by: M. Venkata Krishna Reddy, et al.
Published: (2025-05-01) -
SE-CapsNet: Automated evaluation of plant disease severity based on feature extraction through Squeeze and Excitation (SE) networks and Capsule networks
by: Shradha Verma, et al.
Published: (2021-12-01)