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	Object Recognition and 3D Pose Estimation Using Improved VGG16 Deep Neural Network in Cluttered Scenes
	Shengzhan He; Guoyuan Liang; Fan Chen; Xinyu Wu; Wei Feng
	2018
会议日期	2018
会议地点	Guangzhou,China
英文摘要	3D pose Estimation and object detection are important tasks for robot-environment interaction. Impressive progress has been made in this field over the past decade. So far, the problem is still challenging because cluttered scenes usually have a negative influence on the recognition process. On the other hand, lack of samples in the training stage also limits the application of the learning-based algorithm. In this work, an improved VGG16 deep neural network pipeline which can extract better feature descriptors is proposed to implement object recognition as well as 3D pose estimation. In addition, we also described a method for quick image data synthesis, which can generate large amount of eligible training data in a short period of time. Experimental results demonstrate the effectiveness and better performance of the proposed method by comparing with classical deep neural networks
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13857]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Shengzhan He,Guoyuan Liang,Fan Chen,et al. Object Recognition and 3D Pose Estimation Using Improved VGG16 Deep Neural Network in Cluttered Scenes[C]. 见:. Guangzhou,China. 2018.

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