CP-SSD: Context Information Scene Perception Object Detection Based on SSD

Abstract

Single Shot MultiBox Detector (SSD) has achieved good results in object detection but there are problems such as insufficient understanding of context information and loss of features in deep layers. In order to alleviate these problems, we propose a single-shot object detection network Context Perception-SSD (CP-SSD). CP-SSD promotes the network’s understanding of context information by using context information scene perception modules, so as to capture context information for objects of different scales. Deep layer feature map used semantic activation module, through self-supervised learning to adjust the context feature information and channel interdependence, and enhance useful semantic information. CP-SSD was validated on benchmark dataset PASCAL VOC 2007. The experimental results show that, compared with SSD, the mean Average Precision (mAP) of the CP-SSD detection method reaches 77.8%, which is 0.6% higher than that of SSD, and the detection effect was significantly improved in images with difficult to distinguish the object from the background.

Keywords

• object detection
• deep convolutional neural network
• context feature scene perception
• semantic activation
• parallel dilated convolution