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Proceedings Paper

Obstacle detection and recognition using SSD
Author(s): TaeJun Moon; Noriyuki Nakamura; Tad Gonsalves
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Paper Abstract

Fast obstacle detection is essential for autonomous driving. In this research, we have developed an obstacle detection model using Single Shot Multi Box Detector. SSD is a regression-based object detecting convolutional neural network that takes images as an input to compute localization and classification at once. By using SSD, processing time is dramatically reduced compare to multi shot detector. SSD object detection model was trained using APIs provided by Google in different patterns of number of classes and availability of transfer learning. Increase of the number of classes tended to decrease the detection rate. Training with transfer learning increased the average precision in general. The effectiveness of transfer learning in image recognition can be confirmed. Also there is a difference in average precision depending on the class.

Paper Details

Date Published: 29 October 2018
PDF: 6 pages
Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360M (29 October 2018); doi: 10.1117/12.2514269
Show Author Affiliations
TaeJun Moon, Sophia Univ. (Japan)
Noriyuki Nakamura, Sophia Univ. (Japan)
Tad Gonsalves, Sophia Univ. (Japan)

Published in SPIE Proceedings Vol. 10836:
2018 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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