Mixed text detection and classification method based on attention mechanism and YOLOv7

Lianqiang Niu; Fenglin Lv

doi:10.1117/12.3004419

16 August 2023 Mixed text detection and classification method based on attention mechanism and YOLOv7

Lianqiang Niu, Fenglin Lv

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 1278719 (2023) https://doi.org/10.1117/12.3004419
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

Abstract

To improve the accuracy of mixed text classification, CBAM attention mechanism is introduced in the backbone network of YOLOv7 to highlight the key features of dominant category determination. At the same time, aiming at the information loss of PANet route aggregation network, the ASFF adaptive feature fusion mechanism is embedded after it, and the feature map is adaptively weighted by pixels to better preserve the details of the feature map. Experiments show that the Macro-F1 value is improved by 1.9% compared with PSENet + SVM method which detects and classifies first.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lianqiang Niu and Fenglin Lv "Mixed text detection and classification method based on attention mechanism and YOLOv7", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 1278719 (16 August 2023); https://doi.org/10.1117/12.3004419

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available