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With the rapid development of driverless cars, pedestrian detection has been a canonical instance of object detection. Although recent deep learning detectors such as RPN+BF and MS-CNN have shown excellent performance for pedestrian detection, they have limited success for detecting pedestrian, and the importance of final feature receptive field has been awared by previous leading deep learning pedestrian detectors. Applying the dilated convolution to the feature learning of pedestrian detection, we constructed a pedestrian detection framework along with the region proposal network and boosted decision trees. Pipeline of our proposed framework can be briefly generalized as follows: firstly, the fine-tuned RPN with specified aspect ratio is used to get boxes and scores. Secondly, the designed dilated convolution feature extraction model is used to get features. As different dilation factors provide different …