计算机应用与软件2017，Vol.34Issue(10)：227-231,247,6.DOI:10.3969/j.issn.1000-386x.2017.10.040
一种基于深度学习的新型小目标检测方法
A NEW METHOD OF SMALL TARGET DETECTION BASED ON DEEP-LEARNING
陈江昀1
作者信息
- 1. 浙江工业大学国际学院 浙江杭州310023
- 折叠
摘要
Abstract
Accurate and fast object detection is one of the research topics in computer vision.At present,the general target detection model mainly consists of two parts,the extraction of candidate regions and the design of classifier.This paper innovatively proposes to apply convolutional neural network (CNN) and super pixel to the detection of a new small target.Firstly,we employed SLIC algorithm to over-segment the image.Then,we extracted the features of the over segmentation region and merged the regions.Finally,candidate regions were extracted.Compared with the traditional proposed region extraction method,our proposed method reduced the number of candidate regions on the premise of ensuring recall rate.To overcome the difficulty of feature extraction of small targets,our algorithm used multi-level and multi-layer CNN to extract semantic information of the middle and high level of candidate regions for target classification.Experiment on detecting vehicle inspection mark shows that our method achieves better recall rate (increased by 2％,2.4％,3.5％) compared with the state-of-the-art method including Bing,Selective search,and Edge box.Meanwhile,the multi-level and multi-scale target classification algorithm can effectively reduce the false detection rate and improve the detection rate.关键词
目标检测/CNN/超像素/目标建议法Key words
Object detection/CNN/Super-resolution/Object proposal分类
信息技术与安全科学引用本文复制引用
陈江昀..一种基于深度学习的新型小目标检测方法[J].计算机应用与软件,2017,34(10):227-231,247,6.
