A Comparative Study of Various Learning Models for Object Detection in Contextual Scene Interpretation
文献类型： 会议论文
第一作者： Taranpreet Singh
作者： Taranpreet Singh 1 ; Dr.Hemang Shrivastava 2 ;
作者机构： 1.CSE, Rajarambapu Institute of Technology, Sangli, India
2.CSE, Sage University, Indore, India
关键词： Deep learning;Analytical models;Convolution;Computational modeling;Object detection;Computer networks;Information systems
会议名称： International Conference on Information Systems and Computer Networks
主办单位：
页码： 1-4
摘要： Deep Learning approaches for solving the problem created a revolution in the field of research. An example like scene interpretation which includes the objects detection, classification, and recognition of objects. These deep learning techniques can handle large amounts of structured or unstructured data. This paper shows the comparative analysis of various model which are most preferable in deep learning for the object detection and classification purpose. The model like CNN, YOLO, RNN, RCNN, Fast RCNN, and Faster RCNN is used for object detection and classification purpose with various datasets like Imagenet, PASCAL, and SYSU. RNN model is also used to show the relation between the objects. This paper covers various models of deep learning techniques for detecting and classifying objects from the scene.
分类号： tp393-53
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