Recently, scene text detection based on deep learning has progressed substantially. Nevertheless, most previous models with FPN are limited by the drawback of sample interpolation algorithms, which fail to generate high-quality up-sampled features. Accordingly, we propose an end-to-end trainable text detector to alleviate the above dilemma. Specifically, a Back Projection Enhanced Up-sampling (BPEU) block is proposed to alleviate the drawback of sample interpolation algorithms. It significantly enhances the quality of up-sampled features by employing back projection and detail compensation. Further-more, a Multi-Dimensional Attention (MDA) block is devised to learn different knowledge from spatial and channel dimensions, which intelligently selects features to generate more discriminative representations. Experimental results on three benchmarks, ICDAR2015, ICDAR2017- MLT and MSRA-TD500, demonstrate the effectiveness of our method.