TY  - JOUR
AU  - Diego Velazquez
AU  - Josep M. Gonfaus
AU  - Pau Rodriguez
AU  - Xavier Roca
AU  - Seiichi Ozawa
AU  - Jordi Gonzalez
PY  - 2021//
TI  - Logo Detection With No Priors
T2  - ACCESS
JO  - IEEE Access
SP  - 106998
EP  - 107011
VL  - 9
N2  - In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors.
UR  - https://ieeexplore.ieee.org/document/9502074
UR  - http://dx.doi.org/10.1109/ACCESS.2021.3101297
N1  - ISE
ID  - Diego Velazquez2021
ER  -