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An adaptive representation algorithm for Multi-scale logo detection
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AI
DOI：10.1016/j.displa.2021.102090.png)
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摘要
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The proliferation of logo has driven research into multiple applications, like logo duration monitoring in advertising videos and logo infringement detection. Recently, logo research has attracted a rapt attention and keen interest from researchers. In these studies, logo detection is challenging due to the characteristics of the logo (like multi-scale, large-scale categories, viewpoint and part deformation etc.) and the complexity of its background. In this paper, we design a new strong baseline method based on an adaptive representation algorithm, called obtaining sufficient features for logo (OSF-Logo). The method aims to address the challenges of the multiscale objects, large-scale categories, viewpoint logos and part deformation via introducing two modules. Specifically, we introduce a regulated deformable convolution module with offsets and amplitudes to more convolution layers in the stage of feature extraction. In addition, we add an up-sampling operator to FPN for aggregating information into a large receptive field. The experimental results on several publicly available datasets demonstrate the effectiveness of OSF-Logo.
Keyword:
Logo Detection
Feature Recombination
Deformation
Multi-scale
Adaptive
Deep Learning
期刊
IF:
3.4
论文数:
2.2K
被引数:
3.2K
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