object detection framework (Figure \[framework\]). We first introduce the reverse connection on traditional CNNs in Section \[multiscale\], such that different network scales have effective detection capabilities. Then in Section \[refboxes\], we explain how to generate candidate boxes on different network scales. Next, we present the objectness prior to guide the search of objects in Section \[objprior\] and Secction \[detection\_sec\]. Finally, we combine the objectness prior and object detection into a unified network for joint training and testing (Section \[combine\]).

**Network preparation** We use VGG-16 as the test case reference model, which is pre-trained with ImageNet dataset [@vgg16]. Recall that VGG-16 has 13 convolutional layers and 3 fully-connected layers. We convert FC6 (14th layer) and FC7 (15th layer) to convolutional layers [@fcn], and use 2$\times$2 convolutional kernels with stride 2 to reduce the resolution of FC7 by half[^1]. By now, the feature map sizes used for object detection are $1/8$ (conv 4\_3), $1/16$ (conv 5\_3), $1/32$ (conv 6) and $1/64$ (conv 7) of the input size, both in width and height (see Figure \[framework\] top).

Reverse Connection {#multiscale}
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Combining fine-grained details with highly-abstracted information helps object detection with different scales [@hypercol][@fcn][@resnet]. The region-based networks usually fuse multiple CNN layers into 