| Issue |
E3S Web Conf.
Volume 360, 2022
2022 8th International Symposium on Vehicle Emission Supervision and Environment Protection (VESEP2022)
|
|
|---|---|---|
| Article Number | 01068 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202236001068 | |
| Published online | 23 November 2022 | |
Improved Asian food object detection algorithm based on YOLOv5
1 Department of Information Technology, Water Conservancy of Shandong Technician College, China
2 School of Computer Science and Technology, Shandong University of Technology, China
* Corresponding author: handuhandu@163.com
An improved model called TR-YOLO is employed for Asian food object detection. Firstly, the ViT module is introduced into the model to make better use of global features. Secondly, the Swin Transformer module is introduced on the three detection branches to output the features. Finally, the Mconcat feature fusion method is proposed, which enables the model to learn the feature weights to assign feature channels independently. The experimental results show that the TR-YOLO model further improves the detection accuracy.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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