Automated Vehicle Recognition with Deep Convolutional Neural Networks Adu-Gyamfi, Yaw Sharma, Anuj Sharma, Anuj Titus, Tienaah
dc.contributor.department Civil, Construction and Environmental Engineering 2018-04-02T21:24:53.000 2020-06-30T01:12:36Z 2020-06-30T01:12:36Z 2017-01-01
dc.description.abstract <p>In recent years there has been growing interest in the use of nonintrusive systems such as radar and infrared systems for vehicle recognition. State-of-the-art nonintrusive systems can report up to eight classes of vehicle types. Video-based systems, which arguably are the most popular nonintrusive detection systems, can report only very coarse classification levels (up to four classes), even with the best-performing vision systems. The present study developed a vision system that can report finer vehicle classifications according to FHWA’s scheme and is also comparable to other nonintrusive recognition systems. The proposed system decoupled object recognition into two main tasks: localization and classification. It began with localization by generating class-independent region proposals for each video frame, then it used deep convolutional neural networks to extract feature descriptors for each proposed region, and, finally, the system scored and classified the proposed regions by using a linear support vector machines template on the feature descriptors. The precision of the system varied by vehicle class. Passenger cars and SUVs were detected at a precision rate of 95%. The precision rates for single-unit, single-trailer, and double-trailer trucks ranged between 92% and 94%. According to receiver operating characteristic curves, the best system performance can be achieved under free flow, daytime or nighttime, and with good video resolution.</p>
dc.description.comments <p>This article is published as Adu-Gyamfi, Yaw Okyere, Sampson Kwasi Asare, Anuj Sharma, and Tienaah Titus. "Automated Vehicle Recognition with Deep Convolutional Neural Networks." <em>Transportation Research Record: Journal of the Transportation Research Board</em> 2645 (2017): 113-122. DOI: <a href="" target="_blank">10.3141/2645-13</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 1182
dc.identifier.contextkey 11889299
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_pubs/180
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 21:35:21 UTC 2022
dc.source.uri 10.3141/2645-13
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Operational Research
dc.subject.disciplines Transportation Engineering
dc.title Automated Vehicle Recognition with Deep Convolutional Neural Networks
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 717eae32-77e8-420a-b66c-a44c60495a6b
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
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