A major challenge of pedestrian detection is to detect different-scale pedestrians in complicated scenarios, especially for far-scale pedestrians. Motivated by the experiment that pedestrians with different scales exhibit dramatically different visual features, we propose in this paper a multi-scale aware pedestrian detection algorithm. Firstly, we introduce deformable convolutional layers in full convolutional network structure to expand the receptive field of feature maps. Secondly, we use cascade-region proposal network to extract multi-scale pedestrian proposals and introduce discriminant strategy, and define a multi-scale discriminant layer to distinguish pedestrian proposals category. Finally, we construct a multi-scale aware network, use the soft non-maximum suppression algorithm to fuse the output of classification score and regression offsets by each sensing network to generate final pedestrian detection regions. The experiments show that there is low detection error on the datasets Caltech and ETH, and the proposed algorithm is better than the current detection algorithms in terms of detection accuracy and works particularly well with far-scale pedestrians.