Smart scanning technology detects early signs of potholes

Smart scanning technology detects early signs of potholesInter­est­ing work by a team led by Not­ting­ham Trent Uni­ver­si­ty on mak­ing a mobile scan­ner that can detect ear­ly signs of pot­holes. It uses con­ven­tion­al cam­eras, 3D scan­ners and com­put­er vision to detect ‘rav­el­ling’. Mov­ing this around at traf­fic speed mount­ed to a vehi­cle, and com­bin­ing it with GPS and a suit­able log­ging sys­tem, it could be used to improve road con­di­tions.

Researchers are devel­op­ing smart scan­ning tech­nol­o­gy using exist­ing cam­eras to detect the ear­ly signs of pot­holes and deter­mine their sever­i­ty.

The tech­nol­o­gy, devel­oped by a team led by Not­ting­ham Trent Uni­ver­si­ty research fel­low Dr Sen­than Math­a­van, scans roads for rav­el­ling — the loss of aggre­gates from the asphalt which leads to pot­holes and cracks.

Ravelling - an early indication of potholes
Rav­el­ling — an ear­ly indi­ca­tion of pot­holes — Source

Com­bined with 2D and 3D scan­ners on a pave­ment mon­i­tor­ing vehi­cle, a com­put­er vision algo­rithm can exam­ine the road with accu­ra­cy at traf­fic speed dur­ing day or night.

The sys­tem works by detect­ing dif­fer­ent tex­tures of the road to iden­ti­fy rav­el­ling and dis­tin­guish­es it from shad­ows and blem­ish­es such as tire marks, oil spills and recent pot­hole repairs.

“It’s imper­a­tive for author­i­ties across the world to be able to mon­i­tor road con­di­tions effi­cient­ly and safe­ly,” said Dr Math­a­van, a research fel­low of the School of Archi­tec­ture, Design and the Built Envi­ron­ment.

“For the first time, aca­d­e­m­ic research has addressed the issue of detect­ing rav­el­ling in an auto­mat­ed way, which has led to the devel­op­ment of this nov­el soft­ware which can be used across the indus­try.”

The research was pub­lished today in Trans­porta­tion Research Record, a lead­ing aca­d­e­m­ic jour­nal for trans­porta­tion infra­struc­ture research. It also involves Dr Mujib Rah­man of Brunel Uni­ver­si­ty, Mar­tyn Stonecliffe-Jones of Dynat­est UK Ltd, and Dr Khur­ram Kamal of the Nation­al Uni­ver­si­ty of Sci­ences and Tech­nol­o­gy in Pak­istan.

Dur­ing the research, the team found that the tech­nol­o­gy detect­ed road sur­faces cor­rect­ly in all 900 images test­ed. It took approx­i­mate­ly 0.65 sec­onds to 3D process the rav­el­ling mea­sure­ments, but it is believed that this could be reduced fur­ther.

potholeDr Rah­man added: “Pot­holes, in their worst poten­tial form, can cre­ate dan­ger­ous dri­ving con­di­tions and cause cost­ly dam­age to vehi­cles.

“What this tech­nol­o­gy allows us to do is cap­ture bet­ter qual­i­ty infor­ma­tion on road con­di­tions, with­out dis­rupt­ing the flow of traf­fic or incur­ring unnec­es­sary costs.

“This could be a sig­nif­i­cant step for­ward in the way that pot­holes are man­aged, help­ing improve the time­li­ness and effi­cien­cy of repairs.”

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