Paper
9 October 2022 Improved 2D target detection with YoloV5 based on attention mechanism
Yong Zhou, Mei Zhang, Fujin Hou, Bin Lv, Jianqing Wu
Author Affiliations +
Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122460S (2022) https://doi.org/10.1117/12.2643639
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China
Abstract
For 2D image data target detection, this paper proposes an improved detection algorithm for YoloV5s based on the attention mechanism. By replacing the Focus module in YoloV5s with a CBL module, it facilitates model export. The four attention modules, SELayer, EcaLayer, CBAM and CoordAtt are added to the convolutional network of YoLoV5s. Before the P4 layer, before the P5 layer, before the SPPF layer and before the last layer of the original backbone network, respectively. By experimenting with algorithms that include YoloV5s, It was concluded that among the five algorithms tested for evaluation. The proposed YoloV5s-CoordAtt has the best performance level in terms of accuracy, with a 4.48% improvement compared to the original algorithm.
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yong Zhou, Mei Zhang, Fujin Hou, Bin Lv, and Jianqing Wu "Improved 2D target detection with YoloV5 based on attention mechanism", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122460S (9 October 2022); https://doi.org/10.1117/12.2643639
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Detection and tracking algorithms

Target detection

3D modeling

Convolution

Databases

Head

Information fusion

Back to Top