reverse connection block will be generated in the same way, as shown in Figure \[framework\]. In total, there are four reverse fusion maps with different scales.

Compared with methods using single layer for object detection [@hypernet][@fasterrcnn], multi-scale representation is more effective on locating all scales of objects (as shown in experiments). More importantly, as the reverse connection is learnable, the semantic information of former layers can be significantly enriched. This characteristic makes RON more effective in detecting all scales of objects compared with [@ssd].

Reference Boxes {#refboxes}
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In this section, we describe how to generate bounding boxes on feature maps produced from Section \[multiscale\]. Feature maps from different levels within a network are known to have different receptive field sizes [@sppnet]. As the reverse connection can generate multiple feature maps with different scales. We can design the distribution of boxes so that specific feature map locations can be learned to be responsive to particular scales of objects. Denoting the minimum scale with $s_{min}$, the scales $S_k$ of boxes at each feature map $k$ are $$S_k = \{(2k-1)\cdot s_{min},  2k\cdot s_{min}\}, k\in\{1,2,3,4\}.
\label{boxes}$$ We also impose different aspect ratios {$\frac{1}{3}, \frac{1}{2}, 1, 2, 3$} for the default boxes. The width 