{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:31:36Z","timestamp":1787167896007,"version":"build-2736575974"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T00:00:00Z","timestamp":1515542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2015H1D3A1062316"],"award-info":[{"award-number":["NRF-2015H1D3A1062316"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u201ctrail\u201d and \u201cnon-trail\u201d 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.<\/jats:p>","DOI":"10.3390\/s18010178","type":"journal-article","created":{"date-parts":[[2018,1,10]],"date-time":"2018-01-10T12:41:10Z","timestamp":1515588070000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Accurate Natural Trail Detection Using a Combination of a Deep Neural Network and Dynamic Programming"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8531-4599","authenticated-orcid":false,"given":"Shyam","family":"Adhikari","sequence":"first","affiliation":[{"name":"Division of Electronics Engineering, Chonbuk National University, Jeonju 567-54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changju","family":"Yang","sequence":"additional","affiliation":[{"name":"Division of Electronics Engineering, Chonbuk National University, Jeonju 567-54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krzysztof","family":"Slot","sequence":"additional","affiliation":[{"name":"Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18\/22, 90-924 Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3321-5695","authenticated-orcid":false,"given":"Hyongsuk","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Electronics Engineering, Chonbuk National University, Jeonju 567-54896, Korea"},{"name":"Intelligent Robot Research Center of Chonbuk National University, Chonbuk National University, Jeonju 567-54896, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,10]]},"reference":[{"key":"ref_1","unstructured":"Chiu, K.Y., and Lin, S.-F. 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