<p>At present, scene text detection is grabbing more and more attention as an offshoot of machine vision. However, due to the existing long types of text instances and complex background context, less exact localization and higher missed detection cases still remain in text detection domain. Accordingly, with the aim of tackling these two issues, we propose a text detector named GP-PSENet that comprises a combination of a group-related dilated encoder, a parallel extensional dilation-wise residual encoder and a mixed upsample. Firstly, feature maps of the lowest level processed by the backbone network are sent to a dilated encoder with group linkage. And the group residual module provides stratification to join group coefficients and dilated factors. This module can enhance the correctness of predictions about longer boundary boxes. Secondly, semantic information from the highest level is fed into a parallel extensional dilation-wise residual encoder. The extensional dilation-wise module is capable of obtaining diverse receptive fields by more parallel branches. And it can alleviate error detection from interfering material in the background. Thirdly, the feature maps processed in the second step are given to the mixed upsample module for transforming so as to the next fuse. Finally, the processed two-level feature maps are fused and sent to the progressive scale expansion algorithm for the final post-processing to gain the predicted coordinate points. Ablation experiments are conducted on CTW1500, ICDAR15, MSRA-TD500 and Total-Text datasets to confirm the availability of the proposed method. The values of precision on these datasets reach 86.24%, 87.84%, 73.98% and 90.48%. The proposed method is also competitive with other scene detection methods.</p>