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.
Highlights
Object detection is one of the main tasks of image processing
The experimental results show that, compared with Single Shot MultiBox Detector (SSD), the mean Average Precision of the Context Perception-SSD (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
We can see that the semantic activation module can improve the performance of the model by
Summary
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. Hand-crafted features lack sufficient discriminative representation, perform poorly in generalization ability and are affected by low contrast quality. It is difficult and time-consuming to perform object detection on a large and complex dataset
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