An Approach Combined the Faster RCNN and Mobilenet for Logo Detection. (August 2019)

• Record Type: Journal Article
• Authors: Mudumbi, Terry; Bian, Naizheng; Zhang, Yiyi; Hazoume, Florian
• Is Part Of: Journal of physics. Volume 1284(2019)
• Publication Date: 2019-08
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1284/1/012072
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Although deep learning object detection tools such as Faster Recurrent Convolution Neural Network (Faster R-CNN) has demonstrated good performances in object detection, they also have a limited success rate for some applications. It is due to the lack of refinedness of feature maps for accurate localization, the insensitivity for small scale objects and fixed-window feature extraction in Region Proposal Network (RPN). In this paper, we performed a meticulous examination of both the proposal and the classification process by evaluating the adequacy of feature representations from different stages of the feature sequencing. We presented an approach to improve the Regional Proposal Network (RPN) by appropriate anchors selection, and proposed a modification by combining Faster R-CNN and MobileNet which influences higher-resolution feature maps for mobile devices. The results demonstrate that Faster R-CNN architecture with MobileNet has the best detection accuracy. The experiment result showed that we managed to achieve a final accuracy of 92.4% on a NVIDIA GeForce Gtx 1070 compare to previous work that achieved 90.8% of accuracy and found that our models performed well at the detection, with very low false positive rates possible for a fairly reasonably.