Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming

This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) con...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-01, Vol.18 (1), p.178
Main Authors: Adhikari, Shyam Prasad, Yang, Changju, Slot, Krzysztof, Kim, Hyongsuk
Format: Article
Language:English
Subjects:
deep neural networks
Dynamic programming
Image classification
Mountains
Neural networks
Rural areas
Rural environments
trail following
trail segmentation
Vision systems
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Summary:This paper presents a vision sensor-based solution to the challenging problem of detecting and following trails in highly unstructured natural environments like forests, rural areas and mountains, using a combination of a deep neural network and dynamic programming. The deep neural network (DNN) concept has recently emerged as a very effective tool for processing vision sensor signals. A patch-based DNN is trained with supervised data to classify fixed-size image patches into "trail" and "non-trail" categories, and reshaped to a fully convolutional architecture to produce trail segmentation map for arbitrary-sized input images. As trail and non-trail patches do not exhibit clearly defined shapes or forms, the patch-based classifier is prone to misclassification, and produces sub-optimal trail segmentation maps. Dynamic programming is introduced to find an optimal trail on the sub-optimal DNN output map. Experimental results showing accurate trail detection for real-world trail datasets captured with a head mounted vision system are presented.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18010178