Title:
ATTENTION MECHANISM-BASED SYSTEM AND METHOD FOR DETECTING FEATURE IN TARGET, AND STORAGE MEDIUM
Document Type and Number:
WIPO Patent Application WO/2022/241803
Kind Code:
A1
Abstract:
The present invention provides an attention mechanism-based system and method for detecting a feature in a target, and a storage medium, and relates to the field of intelligent security. The system comprises a semantic extraction module, an attention map module and a detection module, and a classification sub-module is responsible for carrying out global attribute classification on a target and for supervising the training of an attention sub-module; the attention sub-module is responsible for constructing an attention map; and the detection module comprises an anchor frame filter layer, a target detection layer and a parsing layer, the anchor frame filter layer performing data filtering on a received result of the attention map module, sending the result to the target detection layer and the parsing layer for detection and analysis, and outputting a detection result. In the present invention, a multi-task learning method based on a deep convolutional network is employed, and attention learning and a single-scale detection mechanism are introduced, a feature in a target is detected and positioned, and global attributes of the target are classified and recognized, which solves the problem that in training stages of conventional solutions, sample distribution is unbalanced and the requirement for computing power is high due to multiple anchor frames and multiple scales, thereby improving detection efficiency and precision.
Inventors:
HUANG YUHENG (CN)
WEI DONG (CN)
YUE XUYAO (CN)
JIN XIAOFENG (CN)
XU TIANSHI (CN)
WEI DONG (CN)
YUE XUYAO (CN)
JIN XIAOFENG (CN)
XU TIANSHI (CN)
Application Number:
PCT/CN2021/095956
Publication Date:
November 24, 2022
Filing Date:
May 26, 2021
Export Citation:
Assignee:
GRG BANKING EQUIPMENT CO LTD (CN)
International Classes:
G06N3/04; G06K9/62
Foreign References:
| CN112183414A | 2021-01-05 | |||
| CN112200089A | 2021-01-08 | |||
| CN109993101A | 2019-07-09 | |||
| US20200410235A1 | 2020-12-31 | |||
| US20210012146A1 | 2021-01-14 |
Other References:
WANG, LI ET AL.: "S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection", HTTPS://ARXIV.ORG/PDF/2103.08439, 15 March 2021 (2021-03-15), XP093007671
Attorney, Agent or Firm:
ADVANCE CHINA IP LAW OFFICE (CN)
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