# Meta-learning Convolutional Neural Architectures for Multi-target Concrete Defect Classification with the COncrete DEfect BRidge IMage Dataset

arXiv: https://arxiv.org/abs/1904.08486 · HTML: https://mlsanity.com/paper/1904.08486/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 21 of 21)

- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design](https://mlsanity.com/paper/2311.12668/) (2023)
- [Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics](https://mlsanity.com/paper/2310.17316/) (2023)
- [Designing a Hybrid Neural System to Learn Real-world Crack Segmentation from Fractal-based Simulation](https://mlsanity.com/paper/2309.09637/) (2023)
- [Damage detection using in-domain and cross-domain transfer learning](https://mlsanity.com/paper/2102.03858/) (2021)
- [dacl10k: Benchmark for Semantic Bridge Damage Segmentation](https://mlsanity.com/paper/2309.00460/) (2023)
- [Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition](https://mlsanity.com/paper/2202.07012/) (2022)
- [Representation Learning in a Decomposed Encoder Design for Bio-inspired Hebbian Learning](https://mlsanity.com/paper/2401.08603/) (2023)
- [Event-based Civil Infrastructure Visual Defect Detection: ev-CIVIL Dataset and Benchmark](https://mlsanity.com/paper/2504.05679/) (2025)
- [ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection](https://mlsanity.com/paper/2401.03298/) (2024)
- [StructDamage:A Large Scale Unified Crack and Surface Defect Dataset for Robust Structural Damage Detection](https://mlsanity.com/paper/2603.10484/) (2026)
- [SCNet: A Generalized Attention-based Model for Crack Fault Segmentation](https://mlsanity.com/paper/2112.01426/) (2021)
- [Human-machine knowledge hybrid augmentation method for surface defect detection based few-data learning](https://mlsanity.com/paper/2304.13963/) (2023)
- [dacl1k: Real-World Bridge Damage Dataset Putting Open-Source Data to the Test](https://mlsanity.com/paper/2309.03763/) (2023)
- [Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Dataset](https://mlsanity.com/paper/2404.05183/) (2024)
- [Segmentation Dataset for Reinforced Concrete Construction](https://mlsanity.com/paper/2407.09372/) (2024)
- [On The Relationship between Visual Anomaly-free and Anomalous Representations](https://mlsanity.com/paper/2410.06576/) (2024)
- [AI and Remote Sensing for Resilient and Sustainable Built Environments: A Review of Current Methods, Open Data and Future Directions](https://mlsanity.com/paper/2507.01547/) (2025)
- [TACK Tunnel Data (TTD): A Benchmark Dataset for Deep Learning-Based Defect Detection in Tunnels](https://mlsanity.com/paper/2512.14477/) (2025)
- [Cracks in the Foundation: A Civil Infrastructure Dataset to Challenge Vision Foundation Models](https://mlsanity.com/paper/2605.18413/) (2026)
- [Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement](https://mlsanity.com/paper/2608.23136/) (2026)

## Abstract (arXiv)

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.