Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection

Muhammad Tanveer, Byunghyun Kim, Jonghwa Hong, Sung Han Sim, Soojin Cho

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Innovative concrete structure maintenance now requires automated computer vision inspection. Modern edge computing devices (ECDs), such as smartphones, can serve as sensing and computational platforms and can be integrated with deep learning models to detect on-site damage. Due to the fact that ECDs have limited processing power, model sizes should be reduced to improve efficiency. This study compared and analyzed the performance of five semantic segmentation models that can be used for damage detection. These models are categorized as lightweight (ENet, CGNet, ESNet) and heavyweight (DDRNet-Slim23, DeepLabV3+ (ResNet-50)), based on the number of model parameters. All five models were trained and tested on the concrete structure dataset considering four types of damage: cracks, efflorescence, rebar exposure, and spalling. Overall, based on the performance evaluation and computational cost, CGNet outperformed the other models and was considered effective for the on-site damage detection application of ECDs.

Original languageEnglish
Article number12786
JournalApplied Sciences (Switzerland)
Volume12
Issue number24
DOIs
StatePublished - Dec 2022

Keywords

  • computer vision
  • damage detection
  • deep learning
  • edge computing device
  • lightweight models

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