CCMIM: Optimizing concrete defect detection through state-space modeling and dynamic feature fusion
Xiaozhen Li
TL;DR
This paper introduces CCMIM, a new framework for detecting concrete cracks that improves accuracy and efficiency compared to existing methods.
Contribution
The novel CCMIM framework combines state-space modeling and dynamic feature fusion for efficient and accurate concrete defect detection.
Findings
CCMIM outperforms traditional and transformer-based models in small crack detection across multiple datasets.
The model achieves 89.2% accuracy on the RDD2022 dataset and 88.1% mAP50.
The Sparse Pyramid Transformer module reduces computation without sacrificing accuracy.
Abstract
Concrete defect detection is crucial to the safety, reliability, and durability of structures. For CNN models, it is impossible to obtain all information at different scales and complex backgrounds, nor can it capture all contexts globally. Transformer-based models are computationally intensive, making it difficult to generalize to real-time detection tasks. To address these issues, we propose a novel end-to-end concrete crack detection framework: Concrete Crack Mamba-in-Mamba (CCMIM). Specifically, we introduce the Mamba-In-Mamba (MiM) module to capture long-range dependencies and global context to improve the concrete defect detection capability based on hierarchical data flow. In addition, this paper also proposes the Dynamic Dual Fusion (DDF) module, which enhances the robustness and adaptability of the model and achieves smooth multi-scale fusion by dynamically changing the feature…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Advanced Neural Network Applications · Domain Adaptation and Few-Shot Learning