Structural Improvements of Single Stage Networks for Object Detection

Object Detection;Neural Network;Deep Learning;620.5

융합과학기술대학원 융합과학부 ;

University:서울대학교 대학원

关键词: Object Detection;    Neural Network;    Deep Learning;    620.5;   

Others  :  http://s-space.snu.ac.kr/bitstream/10371/142273/1/000000149643.pdf

美国|英语

来源: Seoul National University Open Repository

【 摘 要 】

Recently, a lot of single stage detectors using multi-scale features have been actively proposed. They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances. However, the feature maps in the lower layers close to the input which are responsible for detecting small objects in a single stage detector have a problem of insufficient representation power because they are too shallow. There is also a structural contradiction that the feature maps have to deliver low-level information to next layers as well as contain high-level abstraction for prediction. In this paper, we propose a method to enrich the representation power of feature maps using Resblock and deconvolution layers. In addition, a unified prediction module is applied to generalize output results and boost earlier layers’representation power for prediction. The proposed method enables more precise prediction, which achieved higher score than SSD on PASCAL VOC and MS COCO. In addition, it maintains the advantage of fast computation of a single stage detector, which requires much less computation than other detectors with similar performance.