Show simple item record

dc.contributor.authorXu, Xianghua
dc.contributor.authorJin, Jiancheng
dc.contributor.authorZhang, Shanqing
dc.contributor.authorZhang, Lingjun
dc.contributor.authorPu, Shiliang
dc.contributor.authorChen, Zongmao
dc.date.accessioned2020-07-31T01:10:13Z
dc.date.available2020-07-31T01:10:13Z
dc.date.issued2019
dc.identifier.issn0167-739X
dc.identifier.doi10.1016/j.future.2018.11.027
dc.identifier.urihttp://hdl.handle.net/10072/395985
dc.description.abstractDetection and recognition of road traffic signs constitute an important element in Advanced Driver Assistance Systems (ADAS), which can provide real-time road sign perception information to vehicles. In this paper, we proposed a new traffic sign detection method based on adaptive color threshold segmentation and the hypothesis testing of shape symmetry by leveraging traffic signs and image data. First, we calculated an adaptive segmentation threshold using the cumulative distribution function of the image histogram. Based on this, we designed an approximate maximum and minimum normalization method, which is used to suppress the interference of high brightness area and background in image thresholding processes. Secondly, we transformed the highlight shape feature of thresholding image into a connected domain feature vector. And we formulated a shape symmetry detection algorithm based on statistical hypothesis testing to efficiently extract the ROI of traffic signs based on traffic data analysis. We performed some comprehensive experiments on the GTSDB (German Traffic Sign Detection Benchmark) dataset. The accuracy of traffic sign detection exceeded 94%. This method has higher detection accuracy and time efficiency than other methods, and better robustness under complex traffic environment.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom381
dc.relation.ispartofpageto391
dc.relation.ispartofjournalFuture Generation Computer Systems
dc.relation.ispartofvolume94
dc.subject.fieldofresearchComputer Software
dc.subject.fieldofresearchDistributed Computing
dc.subject.fieldofresearchInformation Systems
dc.subject.fieldofresearchcode0803
dc.subject.fieldofresearchcode0805
dc.subject.fieldofresearchcode0806
dc.subject.keywordsScience & Technology
dc.subject.keywordsComputer Science, Theory & Methods
dc.subject.keywordsTraffic sign detection
dc.titleSmart data driven traffic sign detection method based on adaptive color threshold and shape symmetry
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationXu, X; Jin, J; Zhang, S; Zhang, L; Pu, S; Chen, Z, Smart data driven traffic sign detection method based on adaptive color threshold and shape symmetry, Future Generation Computer Systems, 2019, 94, pp. 381-391
dc.date.updated2020-07-30T00:08:15Z
gro.hasfulltextNo Full Text
gro.griffith.authorZhang, Shanqing


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record