# CP-SSD: Context Information Scene Perception Object Detection Based on SSD
## 2 Related Work  
In recent years, the CNN has achieved great success in computer vision tasks, such as image classification [8; 9; 10; 11; 12], segmentation [13; 14] and object detection [3; 4; 15; 16; 17; 18; 19; 20; 21]. Among them, object detection is a basic task that has been extensively studied. There are two frameworks for the network proposed by the research community for object detection--the two-stage framework and the a single-stage framework. In the two-stage framework, such as the R-CNN series, region proposals with different scales and aspect rates are predicted by extracting feature maps using CNN. Then, classification and regression are carried out based on features extracted from the region proposals. With the help of R-CNN [3], the deep learning mechanism was introduced into object detection for the first time in Reference [3], and the algorithm for adaptive object detection was proposed. It generates region proposals which define the set of candidate detections available to the detector. Each region proposal is input into a large CNN to extract features and then use category-specific linear Support Vector Machines (SVMs) for classification. Finally, the object is detected by regression correction. In SPP-Net [17], the spatial pyramid pooling layer is added into the network behind the R-CNN network structure, which overcomes the shortcomings of R-CNN requirements for the size of the input region proposals and hence it helps to improve detection accuracy. For Fast R-CNN [18], a region of interest (RoI) pooling layer based on SPP-Net is introduced, which reduced the number of convolution layers and greatly improved the detection speed. For Faster R-CNN, the regional proposal network (RPN) is introduced in the process of extracting region proposals by Fast R-CNN. The regional proposal is generated on the convolution feature diagram of the last layer of RPN module and they are input into the RoI pooling layer of Fast R-CNN, so as to optimize the selection of the regional proposal, to reduce repeated feature extraction and to improve the accuracy of regional extraction and network training speed. The RoI pooling layer of Faster R-CNN [19] to RoAlign by Mask R-CNN [20] improved and the bilinear interpolation method is adopted to reduce the position error of the boundary regression box. Meanwhile, a Mask generation task was added to improve the detection accuracy to some extent.  
In single-stage frameworks, such as the You Only Look Once (YOLO) [21] series and SSD, the object classifiers and regressions are applied in a dense manner, without the need for object-based pruning. They all classify and regress a set of pre-computed anchors. In the YOLO alogrtim, the end-to-end training mode is used to reduce the network structure. Although the accuracy is slightly worse than that of the R-CNN series, the speed is much faster than that of the R-CNN series. It reduces the error rate of background image detection and enhances the global information of the image. SSD also has a great improvement in speed, and uses a backbone network (for example, VGG16) to generate a low-level detection feature map. Based on this, it constructs several layers of object detection feature maps to learn semantic information in a layered manner, with lower layers detecting smaller objects and higher layers detecting larger objects, so as to eliminate region proposals and subsequent pixel resampling stages.