in computer vision area. It aims to localize and recognize every object instance with a bounding box [@pascalvoc][@coco]. Before the success of deep CNNs [@alexnet], the widely used detection systems are based on the combination of independent components (HOG [@hog], SIFT [@sift] et al.). The DPM [@dpm] and its variants [@dpm1][@fastdpm] have been the dominant methods for years. These methods use object component descriptors as features and sweep through the entire image to find regions with a class-specific maximum response. With the great success of the deep learning on large scale object recognition, several works based on CNNs have been proposed [@cnn1][@overfeat]. R-CNN [@girshick2014rich] and its variants usually combine region proposals (generated from Selective Search [@ss], Edgeboxes [@edgeboxes], MCG [@mcg], et al.) and ConvNet based post-classification. These methods have brought dramatic improvement on detection accuracy [@alexnet]. After the original R-CNN, researches are improving it in a variety of ways. The SPP-Net [@sppnet] and Fast R-CNN [@frcnn] speed up the R-CNN approach with RoI-Pooling (Spatial-Pyramid-Pooling) that allows the classification layers to reuse features computed over CNN feature maps. Under Fast R-CNN pipeline, several works try to improve the detection speed and accuracy, with more effective region proposals [@fasterrcnn], multi-layer fusion [@ion][@hypernet], 