Detecting Pavement Distresses Using Crowdsourced Dashcam Camera Images

Simple item page
dc.contributor.authorDadashova, Baharen
dc.contributor.authorDobrovolny, Chiara Silvestrien
dc.contributor.authorTabesh, Mahmooden
dc.date.accessioned2022-08-12T19:13:43Zen
dc.date.available2022-08-12T19:13:43Zen
dc.date.issued2021-05en
dc.description.abstractPavements play a vital role in the transportation infrastructure in the United States. Damage to public road transportation infrastructure causes roadways to fail to perform as intended and increases crash risks. Road damage must be detected quickly and accurately in order to maintain roads and effectively allocate repair money. In this study, four deep-learning object detection models were used for detecting five types of pavement damages using Nexar Dashcam images. The single-shot multi-box detector (SSD) and faster region-based convolutional neural networks (Faster R-CNN) object detection models using MobileNet and Inception techniques were applied to the selected and processed images. An attempt was made to detect alligator cracking, longitudinal cracking,transverse cracking, and patching on the roadway surface using the developed model. A total number of 6,326 images with a total number of 8,970 damage instances were selected from the provided data. Faster R-CNN models showed higher recall and precision compared to the SSD models. Faster R-CNN Inception Resnet showed the highest average precision of 56%, followed by Faster R-CNN Inception with an average precision of 50%. Recall values showed almost the same trend as precision. Faster R-CNN showed the highest average recall of 43%. The results showed that Type-I and Type-II damages were detected with the highest recall and precision compared to other damages. Type-I damage was detected with an average precision of 81% and recall of 70 % in all of the models.en
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/111515en
dc.language.isoenen
dc.publisherSAFE-D: Safety Through Disruption National University Transportation Centeren
dc.relation.ispartofseriesSafe-D;TTI-Student-07en
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectPavement damage detectionen
dc.subjectMachine visionen
dc.subjectObject detectionen
dc.subjectSSDen
dc.subjectFaster RCNNen
dc.subjectDashcam video imageen
dc.titleDetecting Pavement Distresses Using Crowdsourced Dashcam Camera Imagesen
dc.typeReporten
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
TTI-Student-07_Final-Research-Report_Final-Ready-for-Site-V1-Complete.pdf
Size:
1.83 MB
Format:
Adobe Portable Document Format
Description:
Report
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Safety through Disruption (SAFE-D) University Transportation Center (UTC)