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Research on Scene Parsing Algorithm Cascading Object Detection Network
- Source :
- CSE/EUC (1)
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Using deep learning for visual scene parsing will satisfy the demand of the next generation of automatic driving technology. However, current parsing algorithms are not mature enough for practical applications unless higher accuracy and efficiency are obtained. We propose a novel scene parsing algorithm framework which integrates the object detection technologies into convolution neural network to improve the overall effectiveness. The framework consists of three components: i) a scene parsing network presenting primary semantic segmentation result. ii)an object detection network calculating the location and confidence of the targets in images. iii) an integration and filter module that cascades previous two results. Extensive experiments suggest that our model is capable of practical use and achieving more favorable scene parsing performance of mIoU score as 69.4% on CamVid dataset.
- Subjects :
- Parsing
Computer science
business.industry
Deep learning
Pattern recognition
02 engineering and technology
Image segmentation
Filter (signal processing)
010501 environmental sciences
Semantics
computer.software_genre
01 natural sciences
Convolutional neural network
Object detection
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Segmentation
Computer vision
Artificial intelligence
business
computer
Algorithm
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)
- Accession number :
- edsair.doi...........a37d2476c3d100b8204446d521e758c2