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Proceedings Paper

Monocular vision avoidance method based on fully convolutional networks
Author(s): Ming Chang; Ming Liu; Yuejin Zhao; Liquan Dong; Mei Hui; Lingqin Kong
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Paper Abstract

Visual obstacle avoidance is a practical application of machine vision technology. With the development of unmanned and artificial intelligence, visual obstacle avoidance technology has become a research hotspot, because the avoiding obstacle is an indispensable ability for robots to explore the unknown world. The traditional methods often rely on edge detection or feature point extraction, which has poor robustness and is difficult to meet practical applications. Convolutional neural networks (CNNs) shine in a variety of machine vision problems (image classification, target detection, image segmentation, image generation, etc.), showing an obviously robustness over traditional algorithms. Based on this, this paper proposes a method to solve the task of avoiding obstacle by using the Fully convolutional networks (FCNs) to extract accessible area. This paper also proves the robustness and effectiveness of the method through a series of experiments.

Paper Details

Date Published: 2 November 2018
PDF: 6 pages
Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 108170A (2 November 2018); doi: 10.1117/12.2501978
Show Author Affiliations
Ming Chang, Beijing Institute of Technology (China)
Ming Liu, Beijing Institute of Technology (China)
Yuejin Zhao, Beijing Institute of Technology (China)
Liquan Dong, Beijing Institute of Technology (China)
Mei Hui, Beijing Institute of Technology (China)
Lingqin Kong, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10817:
Optoelectronic Imaging and Multimedia Technology V
Qionghai Dai; Tsutomu Shimura, Editor(s)

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