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Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset

Martin Mundt, Sagnik Majumder, Sreenivas Murali, Panagiotis Panetsos, Visvanathan Ramesh
CVPR 2019
doi:10.1109/CVPR.2019.01145 /cvpr/2019/mundt2019cvpr-metalearning/

Abstract

Recognition of defects in concrete infrastructure, especially in bridges, is a costly and time consuming crucial first step in the assessment of the structural integrity. Large variation in appearance of the concrete material, changing illumination and weather conditions, a variety of possible surface markings as well as the possibility for different types of defects to overlap, make it a challenging real-world task. In this work we introduce the novel COncrete DEfect BRidge IMage dataset (CODEBRIM) for multi-target classification of five commonly appearing concrete defects. We investigate and compare two reinforcement learning based meta-learning approaches, MetaQNN and efficient neural architecture search, to find suitable convolutional neural network architectures for this challenging multi-class multi-target task. We show that learned architectures have fewer overall parameters in addition to yielding better multi-target accuracy in comparison to popular neural architectures from the literature evaluated in the context of our application.

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Cite

Text

Mundt et al. "Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.01145

Markdown

[Mundt et al. "Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/mundt2019cvpr-metalearning/) doi:10.1109/CVPR.2019.01145

BibTeX

@inproceedings{mundt2019cvpr-metalearning,
  title     = {{Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset}},
  author    = {Mundt, Martin and Majumder, Sagnik and Murali, Sreenivas and Panetsos, Panagiotis and Ramesh, Visvanathan},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.01145},
  url       = {https://mlanthology.org/cvpr/2019/mundt2019cvpr-metalearning/}
}