ResNect: An Accurate and Efficient Backbone Network for Text Detection Model
Bowei Zhang, Weifeng Sun, Minghui Ji, Kelong Meng · 2022
As an instance segmentation model, Mask R-CNN can be well applied to text detection tasks, but the accuracy and efficiency of its backbone network, such as ResNet or ResNeXt, are relatively low. To improve the accuracy and computational efficiency, we propose a novel backbone network for Mask R-CNN, called ResNect (Residual Network with channel mixing). ResNect increases model accuracy (reflected by the F1 score on MTWI dataset) by mixing multi-scale features, and improves the efficiency (reflected by the runtime tested on CIFAR-100 dataset) of the backbone network by reducing the module expansion. Through these two methods, the computational requirements are reduced while increasing the accuracy. The experimental results show that, compared with the backbone networks ResNet, ResNeXt and Res2Net, the runtime of ResNect tested on CIFAR-100 is reduced by 15.8%, 34.5% and 29.3%, and the Mask R-CNN with ResNect as the backbone network also has the highest F1 score on MTWI.