Deep Learning for Object Detection and Grasping: A Survey

Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detect...

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Bibliographic Details
Published in2018 IEEE International Conference on Information and Automation (ICIA) pp. 427 - 432
Main Authors Jia, Qun, Cai, Jun, Cao, Zhiqiang, Wu, Yelan, Zhao, Xionglei, Yu, Junzhi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2018
Subjects
Deep learning
Detectors
Feature extraction
Grasping
Object detection
object grasping
Robots
Training
Visual perception and representation
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Summary:Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detection framework based on deep learning, and supplies the latest research results of object grasping with deep learning. Firstly, we briefly analyze the object detection and grasping. Then, the representative object detection methods based on deep learning are overviewed. Thirdly, we introduce the application of convolutional neural networks in object grasping. Finally, the potential trends in object detection and grasping based on deep learning are discussed.
DOI:10.1109/ICInfA.2018.8812318