Scene text detection has achieved impressive progress over the past years. However, there are still two challenges for text detection. The first challenge is the limitation of receptive field on account of the large aspect ratio of texts. The second one is the loss of spatial information due to the long path between lower layers and topmost feature. To address the two problems, we propose an effective text detection network for multi-oriented text. In this paper, we introduce Hybrid feature enhancement module (HFEM) and Low-level feature refinement module (LFRM). HFEM is a multiple parallel branches module to enlarge receptive field and capture multi-scale information for better detection. LFRM is proposed to suppress background noise and strengthen feature propagation. What's more, low-level feature compensation mechanism preserves rich spatial information for the model. Experiments on datasets including ICDAR 2015, MSRA-TD500 and MLT-2017 validate that the proposed method is effective for multi-oriented text. We also provide ablation experiments on ICDAR 2015 to indicate the effectiveness of proposed modules in our network.