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Millions of people die or sustain physical injury yearly as a result of traffic accidents. Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving. In this study, we have trained a nighttime road-marking detection model using NIR camera images. We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique. We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features. The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network. The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm. We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.<\/jats:p>","DOI":"10.1155\/2019\/7174602","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T18:31:22Z","timestamp":1577471482000},"page":"1-11","source":"Crossref","is-referenced-by-count":7,"title":["Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network"],"prefix":"10.1155","volume":"2019","author":[{"given":"Junping","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Vehicle Engineering, College of Mechanical and Electrical Engineering, Central South University, 932 Lushan South Road, Changsha, 410083 Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5659-8628","authenticated-orcid":true,"given":"Shitu","family":"Abubakar","sequence":"additional","affiliation":[{"name":"Department of 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