Monocular vision avoidance method based on fully convolutional networks

Ming Chang; Ming Liu; Yuejin Zhao; Liquan Dong; Mei Hui; Lingqin Kong

doi:10.1117/12.2501978

2 November 2018 Monocular vision avoidance method based on fully convolutional networks

Ming Chang, Ming Liu, Yuejin Zhao, Liquan Dong, Mei Hui, Lingqin Kong

Proceedings Volume 10817, Optoelectronic Imaging and Multimedia Technology V; 108170A (2018) https://doi.org/10.1117/12.2501978
Event: SPIE/COS Photonics Asia, 2018, Beijing, China

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.

Citation Download Citation

Ming Chang, Ming Liu, Yuejin Zhao, Liquan Dong, Mei Hui, and Lingqin Kong "Monocular vision avoidance method based on fully convolutional networks", Proc. SPIE 10817, Optoelectronic Imaging and Multimedia Technology V, 108170A (2 November 2018); https://doi.org/10.1117/12.2501978

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