Evaluation and optimisation of pre-trained CNN models for asphalt pavement crack detection and classification

Sandra Matarneh, Faris Elghaish*, Farzad Pour Rahimian, Essam Abdellatef, Sepehr Abrishami

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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This study explored the performance of ten pre-trained CNN architectures in detecting and classifying asphalt pavement cracks from images. A comparison of eight optimisation techniques led to developing an optimised pre-trained CNN model tailored for crack classification, with DenseNet201 emerging as the most effective, closely followed by ShuffleNet and ResNet101. Conversely, VGG16 exhibited notably lower accuracy among the models evaluated. Through the application of diverse feature selection techniques as optimisers, DenseNet201 consistently outperformed others, followed by DarkNet19 and Xception. Despite employing different optimisers, VGG16 and VGG19 consistently demonstrated inferior performance. The research introduced a novel approach utilising the DenseNet201 model and the GWO optimiser for asphalt pavement crack classification, validated against various CNN models. Its robustness was verified by testing against images contaminated with differing levels and types of noise, yielding promising outcomes. Results underscore the method's potential for accurately detecting diverse crack types, implying applicability in real-world scenarios.

Original languageEnglish
Article number105297
Number of pages16
JournalAutomation in Construction
Early online date31 Jan 2024
Publication statusPublished - 1 Apr 2024


  • Asphalt pavement
  • CNN
  • Cracks classification
  • Deep learning
  • OpDenseNet201
  • Optimisation
  • Pre-trained models

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