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) 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.

[1]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[3]  Xin Zhang,et al.  End to End Learning for Self-Driving Cars , 2016, ArXiv.

[4]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[5]  Sheng-Fuu Lin,et al.  Lane detection using color-based segmentation , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[6]  Jianxiong Xiao,et al.  DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Yan Lu,et al.  Appearance contrast for fast, robust trail-following , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[9]  Jürgen Schmidhuber,et al.  A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots , 2016, IEEE Robotics and Automation Letters.

[10]  Jean Ponce,et al.  General Road Detection From a Single Image , 2010, IEEE Transactions on Image Processing.

[11]  Gustavo Carneiro,et al.  Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue , 2016, ECCV.

[12]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Seunghoon Hong,et al.  Learning Deconvolution Network for Semantic Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Nikolai Smolyanskiy,et al.  Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[15]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[16]  Sebastian Thrun,et al.  Self-supervised Monocular Road Detection in Desert Terrain , 2006, Robotics: Science and Systems.

[17]  Nelson Alves,et al.  Tracking natural trails with swarm‐based visual saliency , 2013, J. Field Robotics.

[18]  Sibel Yenikaya,et al.  Keeping the vehicle on the road: A survey on on-road lane detection systems , 2013, CSUR.

[19]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Fernando A. Mujica,et al.  An Empirical Evaluation of Deep Learning on Highway Driving , 2015, ArXiv.

[21]  John Flynn,et al.  Deep Stereo: Learning to Predict New Views from the World's Imagery , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Yann LeCun,et al.  Learning long‐range vision for autonomous off‐road driving , 2009, J. Field Robotics.

[23]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[24]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[25]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[26]  Ronen Lerner,et al.  Recent progress in road and lane detection: a survey , 2012, Machine Vision and Applications.

[27]  Qi Wang,et al.  Embedding structured contour and location prior in siamesed fully convolutional networks for road detection , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Donald Scott,et al.  Shape-guided superpixel grouping for trail detection and tracking , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Qi Wang,et al.  Video-based road detection via online structural learning , 2015, Neurocomputing.