GCCNet: Grouped channel composition network for scene text detection
| Autor: | Xiaobin Zhu, Lei Xiao, Long-Huang Wu, Jie-Bo Hou, Chun Yang, Xu-Cheng Yin, Chang Liu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Ground truth Computer science Intersection (set theory) Cognitive Neuroscience 02 engineering and technology Text detection Composition (combinatorics) computer.software_genre Computer Science Applications Weighting 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer Block (data storage) Communication channel |
| Zdroj: | Neurocomputing. 454:135-151 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2021.04.095 |
| Popis: | Anchor mechanism is widely applied in scene text detection methods and demonstrates promising performance. However, existing anchor mechanisms have two major limitations, namely handcrafted anchor design and hard-wired anchor assignment. We propose a novel Grouped Channels Composition(GCC) block to achieve the data-driven anchor design and adaptive anchor assignment. To be more specific, our GCC block uses optimizable anchor functions rather than handcrafted ones to achieve data-drive anchor design. In our GCC block, an adaptive anchor assignment is achieved with the attention mechanism instead of empirically assigning anchor according to the Intersection Over Union (IoU) between ground truth and targets. We then build a corresponding network named GCCNet with our GCC blocks. We also propose a Unified Loss Weighting module to alleviate the inconsistency between classification score and localization accuracy. Experiments conducted on publicly available datasets demonstrate the state-of-the-art performance of our methods. |
| Databáze: | OpenAIRE |
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