context information [@resnet][@mrcnn] and more effective training strategy [@ohem]. R-FCN [@rfcn] tries to reduce the computation time with position-sensitive score maps under ResNet architecture [@resnet]. Thanks to the FCNs [@fcn] which could give position-wise information of input images, several works are trying to solve the object detection problem by FCNs. The methods skip the region proposal generation step and predict bounding boxes and detection confidences of multiple categories directly [@multibox]. YOLO [@yolo] uses the top most CNN feature maps to predict both confidences and locations for multiple categories. Originated from YOLO, SSD [@ssd] tries to predict detection results at multiple CNN layers. With carefully designed training strategies, SSD could get competitive detection results. The main advantage of these methods is high time-efficiency. For example, the feed-forward speed of YOLO is 45 FPS, 9$\times$ faster than Faster R-CNN.

Network Architecture {#network}
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![RON object detection overview. Given an input image, the network firstly computes features of the backbone network. Then at each detection scale: (a) adds reverse connection; (b) generates objectness prior; (c) detects object on its corresponding CNN scales and locations. Finally, all detection results are fused and selected with non-maximum suppression.[]{data-label="framework"}](framework.pdf){width="0.95\linewidth"}

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This section describes RON 