| Full text | |
| Author(s): |
Blanger, Leonardo
;
Hirata, Nina S. T.
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP); v. N/A, p. 5-pg., 2019-01-01. |
| Abstract | |
In this work, we employ deep learning models for detecting QR Codes in natural scenes. A series of different model configurations are evaluated in terms of Average Precision, and an architecture modification that allows detection aided by object subparts annotations is proposed. This modification is implemented in our best scoring model, which is compared to a traditional technique, achieving a substantial improvement in the considered metrics. The dataset used in our evaluation, with bounding box annotations for both QR Codes and their Finder Patterns (FIPs), will be made publicly available. This dataset is significantly bigger than known available options at the moment, so we expect it to provide a common benchmark tool for QR Code detection in natural scenes. (AU) | |
| FAPESP's process: | 17/25835-9 - Understanding images and deep learning models |
| Grantee: | Nina Sumiko Tomita Hirata |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 18/00390-7 - QR code detection using deep learning models |
| Grantee: | Leonardo Blanger |
| Support Opportunities: | Scholarships in Brazil - Master |
| FAPESP's process: | 15/22308-2 - Intermediate representations in Computational Science for knowledge discovery |
| Grantee: | Roberto Marcondes Cesar Junior |
| Support Opportunities: | Research Projects - Thematic Grants |