Logo Detection Using Deep Learning with Pretrained CNN Models
Logo detection in images and videos is considered a key task for various applications, such as vehicle logo detection for traffic-monitoring systems, copyright infringement detection, and contextual content placement. The main contribution of this work is the application of emerging deep learning techniques to perform brand and logo recognition tasks through the use of multiple modern convolutional neural network models. In this work, pre-trained object detection models are utilized in order to enhance the performance of logo detection tasks when only a portion of labeled training images taken in truthful context is obtainable, evading wide manual classification costs. Superior logo detection results were obtained. In this study, the FlickrLogos-32 dataset was used, which is a common public dataset for logo detection and brand recognition from real-world product images. For model evaluation, the efficiency of creating the model and of its accuracy was considered.
Keywords:logo detection, deep learning, convolutional neural networks, FlickrLogos-32
M M. Bastan, H.-Y. Wu, T. Cao, B. Kota, and M. Tek, "Large Scale Open-Set Deep Logo Detection," Nov. 2019, Accessed: Jan. 08, 2021. [Online]. Available: http://arxiv.org/abs/1911.07440.
G. Oliveira, X. Frazao, A. Pimentel, and B. Ribeiro, "Automatic graphic logo detection via Fast Region-based Convolutional Networks," in International Joint Conference on Neural Networks, Vancouver, Canada, Jul. 2016, pp. 985-991. https://doi.org/10.1109/IJCNN.2016.7727305
P. Chakraborty and C. Tharini, "Pneumonia and Eye Disease Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5769-5774, Jun. 2020. https://doi.org/10.48084/etasr.3503
M. Salemdeeb and S. Erturk, "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 5979-5985, Aug. 2020. https://doi.org/10.48084/etasr.3573
S. C. H. Hoi et al., "LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks," Nov. 2015, Accessed: Jan. 08, 2021. [Online]. Available: http://arxiv.org/abs/1511.02462.
S. Bianco, M. Buzzelli, D. Mazzini, and R. Schettini, "Deep learning for logo recognition," Neurocomputing, vol. 245, pp. 23-30, Jul. 2017. https://doi.org/10.1016/j.neucom.2017.03.051
S. Yang, J. Zhang, C. Bo, M. Wang, and L. Chen, "Fast vehicle logo detection in complex scenes," Optics & Laser Technology, vol. 110, pp. 196-201, Feb. 2019. https://doi.org/10.1016/j.optlastec.2018.08.007
C. Eggert, D. Zecha, S. Brehm, and R. Lienhart, "Improving small object proposals for company logo detection," in ACM on International Conference on Multimedia Retrieval, New York, USA, Jun. 2017, pp. 167-174. https://doi.org/10.1145/3078971.3078990
H H. Jung et al., "Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network," PLoS ONE, vol. 13, no. 9, Sep. 2018, Art. no. e0203355. https://doi.org/10.1371/journal.pone.0203355
C. Eggert, S. Brehm, A. Winschel, D. Zecha, and R. Lienhart, "A closer look: Small object detection in faster R-CNN," in IEEE International Conference on Multimedia and Expo, Hong Kong, China, Jul. 2017, pp. 421-426. https://doi.org/10.1109/ICME.2017.8019550
H. Su, S. Gong, and X. Zhu, "Scalable logo detection by self co-learning," Pattern Recognition, vol. 97, Jan. 2020, Art. no. 107006. https://doi.org/10.1016/j.patcog.2019.107003
A. Tuzko, C. Herrmann, D. Manger, and J. Beyerer, "Open Set Logo Detection and Retrieval," Oct. 2017, Accessed: Jan. 08, 2021. [Online]. Available: http://arxiv.org/abs/1710.10891.
H. Su, S. Gong, and X. Zhu, "WebLogo-2M: Scalable Logo Detection by Deep Learning from the Web," in IEEE International Conference on Computer Vision Workshops, Venice, Italy, Oct. 2017, pp. 270-279. https://doi.org/10.1109/ICCVW.2017.41
G. Zhu and D. Doermann, "Automatic Document Logo Detection," in Ninth International Conference on Document Analysis and Recognition, Parana, Brazil, Sep. 2007, vol. 2, pp. 864-868. https://doi.org/10.1109/ICDAR.2007.4377038
I. Fehervari and S. Appalaraju, "Scalable Logo Recognition Using Proxies," in IEEE Winter Conference on Applications of Computer Vision, Waikoloa Village, USA, Jan. 2019, pp. 715-725. https://doi.org/10.1109/WACV.2019.00081
P. F. Jaeger et al., "Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection," vol. 1811, Nov. 2018, Accessed: Jan. 08, 2021. [Online]. Available: http://adsabs.harvard.edu/abs/2018arXiv181108661J.
H. Su, X. Zhu, and S. Gong, "Deep Learning Logo Detection with Data Expansion by Synthesising Context," in IEEE Winter Conference on Applications of Computer Vision, Santa Rosa, USA, Mar. 2017, pp. 530-539. https://doi.org/10.1109/WACV.2017.65
A. Alsheikhy, Y. Said, and M. Barr, "Logo Recognition with the Use of Deep Convolutional Neural Networks," Engineering, Technology & Applied Science Research, vol. 10, no. 5, pp. 6191-6194, Oct. 2020. https://doi.org/10.48084/etasr.3734
S. Romberg, L. G. Pueyo, R. Lienhart, and R. van Zwol, "Scalable logo recognition in real-world images," in Proceedings of the 1st ACM International Conference on Multimedia Retrieval, Apr. 2011, Art. no. 25, https://doi.org/10.1145/1991996.1992021. https://doi.org/10.1145/1991996.1992021
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal Loss for Dense Object Detection," in IEEE International Conference on Computer Vision, Venice, Italy, Oct. 2017, pp. 2999-3007. https://doi.org/10.1109/ICCV.2017.324
R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," Oct. 2014, Accessed: Jan. 08, 2021. [Online]. Available: http://arxiv.org/abs/1311.2524. https://doi.org/10.1109/CVPR.2014.81
S S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, Jun. 2017. https://doi.org/10.1109/TPAMI.2016.2577031
F. Al-Azzo, A. M. Taqi, and M. Milanova, "Human Related-Health Actions Detection using Android Camera based on TensorFlow Object Detection API," International Journal of Advanced Computer Science and Applications, vol. 9, no. 10, pp. 9-23, 2018. https://doi.org/10.14569/IJACSA.2018.091002
F. S. Herrera and J. M. Saavedra, "DLDENet: Deep Local Directional Embeddings with Increased Foreground Focal Loss for object detection," in 38th International Conference of the Chilean Computer Science Society, Concepcion, Chile, Nov. 2019, pp. 1-8. https://doi.org/10.1109/SCCC49216.2019.8966436
S. Bianco, M. Buzzelli, D. Mazzini, and R. Schettini, "Logo Recognition Using CNN Features," in Image Analysis and Processing - ICIAP 2015, V. Murino and E. Puppo, Eds. New York, USA: Springer, 2015, pp. 438-448. https://doi.org/10.1007/978-3-319-23234-8_41
F. N. Iandola, A. Shen, P. Gao, and K. Keutzer, "DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer," Oct. 2015, Accessed: Jan. 08, 2021. [Online]. Available: http://arxiv.org/abs/1510.02131.
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