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
4/2/2019
cs.CVcs.LGstat.ML

Official code: MrtnMndt/meta-learning-CODEBRIM (82 stars, last commit 2020-02-24).

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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Code Implementations(1)

MrtnMndt/meta-learning-CODEBRIMOfficial100%

Open-source code for our CVPR19 paper "Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset".

8215Shell, PythonApr 1, 20196 years agoNOASSERTION

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Cite this paper

@article{mundt2019metalearning,
  title  = {Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset},
  author = {Martin Mundt and Sagnik Majumder and Sreenivas Murali and Panagiotis Panetsos and Visvanathan Ramesh},
  year   = {2019},
  eprint = {1904.08486},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1904.08486}
}

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