For example, predicting with Faster R-CNN usually takes 0.2 s per image using about 5GB GPU memory for VGG-16 networks [@vgg16].

Another solution family is the region-free methods [@yolo][@ssd]. These methods treat object detection as a single shot problem, straight from image pixels to bounding box coordinates by fully convolutional networks (FCNs). The main advantage of these detectors is high efficiency. Originated from YOLO [@yolo], SSD [@ssd] tries to deal object detection problem with multiple layers of deep CNNs. With low resolution input, the SSD detector could get state-of-the-art detection results. However, the detection accuracy of these methods still has room for improvement: (a) Without region proposals, the detectors have to suppress all of the negative candidate boxes only at the detection module. It will increase the difficulties on training the detection module. (b) YOLO detects objects with the top-most CNN layer, without deeply exploring the detection capacities of different layers. SSD tries to improve the detection performance by adding former layers’ results. However, SSD still struggles with small instances, mainly because of the limited information of middle layers. These two main bottlenecks affect the detection accuracy of the methods.

Driven by the success of the two solution families, a 