# CP-SSD: Context Information Scene Perception Object Detection Based on SSD
## 1 Introduction  
Object detection is one of the main tasks of image processing. Its main purpose is to be able to accurately locate and classify objects in images. It has been comprehensively used in many communities such as face recognition, road detection, and driverless car, and so forth. The traditional object detection methods such as Histogram of Oriented Gradient (HOG) [1], Scale Invariant Feature Transform (SIFT) [2], are based on hand-crafted features (e.g., RGB color, texture, Gabor filter and gradient). Hand-crafted features lack sufficient discriminative representation, perform poorly in generalization ability and are easily affected by low contrast quality. It is difficult and time-consuming to perform object detection on a large and complex dataset.  
The deep convolutional neural network method promotes the understanding of dynamic objects. However, it still faces the challenge of a lack of rich semantic features and insufficient understanding of context information. In Region-Convolutional Neural Network (R-CNN) series, the selection of regional proposal and repeated convolution of feature maps greatly increases the time and complexity of object detection. R-CNN [3] is several times more accurate than the traditional algorithms based on HOG and SIFT. However, this method of obtaining regional proposals by Selective search in R-CNN will lose a lot of contextual information. In the Single Shot MultiBox Detector (SSD) [4] algorithm, the end-to-end network structure is used to remove the steps in the R-CNN for region proposals. It directly inputs the entire picture into the network structure and predicts objects of different sizes using feature maps of different scales in the Convolutional Neural Network(CNN) [5]. In the backbone network Visual Geometry Group 16(VGG16) [6], low-level detection feature maps are generated andthen several layers of feature detection maps are constructed, so as to learn semantic information in a layered manner. However, for low-level features, there are usually no appropriate strategies to understand contextual information fully, and to capture strong semantic information successfully, which makes it difficult to be understood for complex scenarios. This may result in inaccurate detection of the object and imperfect low-level feature information can cause high-level language information to be affected. At the same time, the receptive field of each layer in the CNN is fixed and there is an inconsistency between objects of different scales in the natural image, which may impair the object detection performance. For small objects, feature extraction is not easy, which may result in a loss of information and the inability to detect small objects.  
According to the above analysis, a new single-shot network model CP-SSD is designed to alleviate the problems in the above SSD. Two modules of context information sensing and semantic activation are added to the original SSD. The context information sensing module uses different convolution kernels to perceive objects of different sizes and combines their important context information. The contextual information of different regions can help to distinguish the goals and backgrounds of various categories more accurately. In addition, inspired by SE-Net [7], a semantic activation module is used on the additional detection feature map built in the SSD. The semantic activation module uses the self-attention mechanism to learn the relationship between the channel and the object, and learn the weights of different channels.