## 3 Proposed Method  
In this section we will detail the architecture of the proposed CISPNet framework. As shown in Figure 3a, the CISPNet assembles four context information scene perception (CISP) modules and two feature fusion modules (FFM) into a conventional SSD. The structure of these additional modules is simple and can be easily combined with conventional detection networks. In the context information scene perception (CISP) module, as shown in Figure 3b, multiple dilated rate convolution layers are used in parallel. Each dilated convolution layer has a different dilated rate, and the size of the dilated rate reflects its corresponding receptive field size. The context information scene perception module uses convolution kernels with different receptive fields to extract features from Conv4_3 and FC7, so that the model can perceive the changes of context information in different scales and sub-domains. In this way, the loss of semantic information can be reduced and make the feature map understand more contextual information from different scales. The inner structure of context information scene perception module is shown in Figure 3b. Firstly, the number of channels of the feature map \(\mathrm{U}\in\mathbb{R}^{\mathrm{WxHyS12}}\) is reduced by using a \(1\times 1\) convolution to obtain a feature map \(\mathrm{X}\in\mathbb{R}^{\mathrm{WxHy256}}\). Then, three kinds of dilated convolutions with different dilated rates \((\mathrm{d}_{1},\mathrm{d}_{2},\mathrm{d}_{3})=(1,2,4)\) are used for feature sampling on the feature map \(\mathrm{X}\) in parallel, and the feature map \(\mathrm{X}_{1}\in\mathbb{R}^{\mathrm{WxHy256}}\), \(\mathrm{X}_{2}\in\mathbb{R}^{\mathrm{WxHy256}}\), and \(\mathrm{X}_{3}\in\mathbb{R}^{\mathrm{WxHy256}}\) are obtained. Finally, the feature map is concatenated to obtain the final feature map \(\mathrm{N}\in\mathbb{R}^{\mathrm{WxHy1024}}\), \(\mathrm{N}=[\mathrm{X},\mathrm{X}_{1},\mathrm{X}_{2},\mathrm{X}_{3}]\). Based on the CISP module, we propose a single-stage detector CISPNet, which can detect small targets more effectively. More specifically, we first assemble two CISP modules between features of the  
Figure 3: The architecture of CISPNet and its novel module CISP. (**a**) The architecture of the CISPNet framework; (**b**) The layer settings of the CISP module, where each box represents a conv+bn+relu group. layers Conv4_3 and FC7 and the layers FC7 and Conv8_2 respectively. In addition, we connect another two separate CISP modules to Conv4_3 and FC7 detection branches respectively, and then generate the new layers Conv4_3 and FC7, respectively. As the layers Conv4_3 and FC7 in the backbone are relatively shallow, and the feature semantic information extraction by a shallow network is less and might not have enough capability to detect small targets, we use CISP modules to enhance Conv4_3 and FC7 features. Step forward, feature fusion strategies always contribute to learning better features from the combination of original features [28; 32]. We also applied this method in CISPNet. In detail, with the help of two FFMs, the new Conv4_3 and FC7 are generated by feature fusion. In order to more clearly show the influence of the proposed CISP module on the image feature extraction, we select a remote sensing image containing a large ship target and three small ship targets in a wide sea area, as the input image and extract the features through SSD and the proposed CISPNet. Figure 4 shows a qualitative comparison of the feature mapping results from our proposed CISPNet and the SSD. With the increase of resolution, the feature map becomes smaller and more abstract, and small-sized objects hardly have a response on the deeper layers. As shown in Figure 4a1,a2,b1,b2, the semantic information of a ship from the feature map of CISPNet's Conv4_3 and FC7 layers is clearer than that extracted by the Conv4_3 and FC7 layers of SSD, in particular, the features extracted by CISPNet can highlight the smaller ship targets.