An Effective and Robust Detector for Logo Detection

In recent years, intellectual property (IP), which represents literary, inventions, artistic works, etc, gradually attract more and more people’s attention. Particularly, with the rise of e-commerce, the IP not only represents the product design and brands, but also represents the images/videos displayed on e-commerce platforms. Unfortunately, some attackers adopt some adversarial methods to fool the welltrained logo detection model for infringement. To overcome this problem, a novel logo detector based on the mechanism of looking and thinking twice is proposed in this paper for robust logo detection. The proposed detector is different from other mainstream detectors, which can effectively detect small objects, long-tail objects, and is robust to adversarial images. In detail, we extend detectoRS algorithm to a cascade schema with an equalization loss function, multi-scale transformations, and adversarial data augmentation. A series of experimental results have shown that the proposed method can effectively improve the robustness of the detection model. Moreover, we have applied the proposed methods to competition ACM MM2021 Robust Logo Detection that is organized by Alibaba on the Tianchi platform and won top 2 in 36489 teams. Code is available at https://github.com/jiaxiaojunQAQ/Robust-Logo-Detection.

[1]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Huajun Feng,et al.  Libra R-CNN: Towards Balanced Learning for Object Detection , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Amy J. C. Trappey,et al.  An intelligent content-based image retrieval methodology using transfer learning for digital IP protection , 2021, Adv. Eng. Informatics.

[5]  Alan Yuille,et al.  DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution , 2020, ArXiv.

[6]  Larry S. Davis,et al.  Soft-NMS — Improving Object Detection with One Line of Code , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[7]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Joana César Machado,et al.  Brand logo design: examining consumer response to naturalness , 2015 .

[9]  Yang Song,et al.  PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples , 2017, ICLR.

[10]  Kaiming He,et al.  Focal Loss for Dense Object Detection , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[12]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[13]  Gang Zhang,et al.  Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Shengjin Wang,et al.  A2-FPN: Attention Aggregation based Feature Pyramid Network for Instance Segmentation , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Caroline Goukens,et al.  Facing a trend of brand logo simplicity: The impact of brand logo design on consumption , 2019, Food Quality and Preference.

[16]  Nuno Vasconcelos,et al.  Cascade R-CNN: Delving Into High Quality Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Huanqian Yan,et al.  Object Hider: Adversarial Patch Attack Against Object Detectors , 2020, ArXiv.

[18]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Xiaochun Cao,et al.  ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[21]  Yuan He,et al.  The Open Brands Dataset: Unified Brand Detection and Recognition at Scale , 2020, ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).