Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming
AbstractThis 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. View Full-Text
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Adhikari, S.P.; Yang, C.; Slot, K.; Kim, H. Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming. Sensors 2018, 18, 178.
Adhikari SP, Yang C, Slot K, Kim H. Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming. Sensors. 2018; 18(1):178.Chicago/Turabian Style
Adhikari, Shyam P.; Yang, Changju; Slot, Krzysztof; Kim, Hyongsuk. 2018. "Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming." Sensors 18, no. 1: 178.
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