One-Stage Logo Detection Framework using AdaBoost Resnet50 Backbone
SSarwo1, Yaya Heryadi2, Widodo Budiharto3, Edi Abdurachman4

1Sarwo*, Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
2Yaya Heryadi, Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
3Widodo Budiharto, Computer Science Study Program, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia.
4Edi Abdurachman, Computer Science Department, BINUS Graduate Program – Doctor of Computer Science, Bina Nusantara University, Jakarta, Indonesia.

Manuscript received on 11 August 2019. | Revised Manuscript received on 20 August 2019. | Manuscript published on 30 September 2019. | PP: 451-457 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4222098319/19©BEIESP| DOI: 10.35940/ijrte.C4222.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Logo is an important asset as it is designed to express identity or character of the company or organization that owns the logo. The advent of deep learning methods and proliferated of logo images sample dataset in the past decade has made automated logo detection from digital images or video an interesting computer vision problem with wide potential applications. This paper presents a novel one-stage logo detector framework in which the backbone of the proposed logo detector is a deep learning model which is trained supervisedly using gradient descent training algorithm and the target logo classes as input dataset. The experiment results showed that AdaBoost Resnet50 (0.58 MAP) as the logo detector backbone outperforms Resnet50 (0.56 MAP), VGG19 (0.32 MAP), and AdaBoost VGG19 (0.56 MAP).
Keywords: Logo Detection, AdaBoost, Resnet, One stage Detector.

Scope of the Article: