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	Object Detection Based on Deep Learning of Small Samples
	Li, Ce1; Zhang, Yachao1; Qu, Yanyun2
	2018
关键词	small examples indoor scene object detection synthetic samples semantic-relevant detection deep learning
页码	449-454
英文摘要	Object detection of indoor scene is widely used in the field of service robot. State-of-art object detectors rely heavily on large-scale datasets like PASCAL VOC2007, VOC2012. However, these approaches fail to indoor scene object detection limited by a few samples and the complex background. This paper presents an object detector based on deep learning of small samples. Firstly, the algorithm can augment training samples automatically by synthetic samples generator to solve the problem of few samples. Synthetic samples generator is designed by switching the object regions in different scenes. Then, deep supervision learning and dense prediction structure are used in the deep convolution neural networks. It is a better solution to solve the vanishing-gradient and the objects with different scale. In addition, the semantic relevance of objects is used to improve the accuracy of weak feature objects in complex scenarios. Experiments on B3DO demonstrate that the proposed algorithm achieves better results than the state-of-art contrast models, and the mean average precision (mAP) was 0.18 higher than the DSOD.
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000455001500079
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36190]
专题	新能源学院 电气工程与信息工程学院
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Gansu, Peoples R China; 2.Xiamen Univ, Coll Informat Sci & Engn, Xiamen 361005, Fujian, Peoples R China
推荐引用方式 GB/T 7714	Li, Ce,Zhang, Yachao,Qu, Yanyun. Object Detection Based on Deep Learning of Small Samples[C]. 见:.

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