Survey on performance of deep learning models for detecting road damages using multiple dashcam image resources

Minh Tu Cao, Quoc Viet Tran, Ngoc Mai Nguyen, Kuan Tsung Chang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


Detecting road damage quickly and accurately facilitates the ability of road-maintenance agencies to make timely repairs to road surfaces, maintain optimal road conditions, optimize transportation safety, and minimize transportation costs. An extensive evaluation of eight deep-learning-based road-damage detection models was conducted in this study. Each model was trained on 9493 images sourced from multiple databases. The 16165 instances of road damage in these images were categorized into five types of damage, including longitudinal crack, horizontal crack, alligator damage, pothole-related crack, and line blurring. Two experiments were conducted that identified two models, single shot multi-box detector (SSD) Inception V2 and faster region-based convolutional neural networks (R-CNN) Inception V2, as providing the best balance of road-damage-detection accuracy and image processing time. These experiments demonstrated that increasing the diversity of image sources improved road-damage-detection model performance. In addition to combining data images from different sources with consistently relabeled damage instances, this study released road-damage image data from the road maintenance agency in Zhubei, Hsinchu County, Taiwan for research and other uses, increasing the limited amount of published image data sources and positively impacting future scholarly research into road damage detection.

Original languageEnglish
Article number101182
JournalAdvanced Engineering Informatics
StatePublished - Oct 2020


  • Convolutional neural network
  • Deep learning
  • Road crack
  • Road damage detection
  • Road maintenance
  • Single shot detection


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