基于改进PVANet的实时小目标检测方法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-12-13 Cooperative journals: 《计算机应用研究》

段秉环 文鹏程 李鹏

Abstract： Existing object detection algorithms are mainly aimed at detecting big objects in an image. Research on small object detection is still too scarce and there are problems with low detection accuracy and failure to meet the real-time requirement. This paper proposed a real-time small object detection method based on deep learning framework PVANet. Firstly, it built a benchmark dataset especially for small object detection problem. The dataset consisted of small objects covering a very small part of an image and also contained some interferences such as truncation and overlap. Secondly, combining with the Region Proposal Network (RPN) , it designed a strategy to generate high-quality candidate proposals for small objects to raise the detection accuracy and speed. Finally, it adopted two new learning rate policies "step" and "inv" to further enhance the detection accuracy. The proposed method achieved the mAP(mean average precision) by 10.67% and speed by 30% improvement over the original PVANet algorithm. Experimental results shows that this method is effective on small object detection and can run in real time.

 Hits 2332  Downloads 1124  Comment