应用掩码区域卷积神经网络的文本检测模型

doi:10.3969/j.issn.0255-8297.2023.03.013

Abstract

Abstract: This paper proposes a text detection model based on mask region convolution neural network (Mask R-CNN). Firstly, the model optimizes the bottleneck structure of residual networks from the perspective of expanding the receptive field of the model and maintaining the efficiency of the model as much as possible, and proposes a residual network based on structural optimization (ResNetSO). Then for removing redundant features and improving the quality of fused features, the model generates a feature pyramid network based on lower feature guidance (FPNetLFG) by applying spatial attention mechanism to feature pyramid network. Finally, experimental results on two data sets show that as applying the proposed model, which consists of ResNetSO and FPNetLFG modules, in cascade region convolution neural network (Cascade R-CNN) and detecting objects with recursive feature pyramid and switchable atrous convolution (DetectoRS), F1 value can be improved by 0.8% and 0.3%, respectively, which verifies the effectiveness and universal applicability of this method.

Key words: text detection, mask region convolution neural network (Mask R-CNN), backbone network, structural optimization, feature pyramid network

CLC Number:

TP391

ZHAO Xiaowei, JI Minghui, XU Xiujuan, SHEN Jiale. Text Detection Model Based on Mask Region Convolution Neural Network[J]. Journal of Applied Sciences, 2023, 41(3): 527-540.

References

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Text Detection Model Based on Mask Region Convolution Neural Network

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