Intelligent Infrastructure Crack Detection Using MSEDBO-Optimized Deep Learning

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Infrastructure surface crack detection is a vital task in structural health monitoring, directly influencing the safety, durability, and serviceability of civil engineering assets. Although deep learning methods have achieved notable success in automated crack detection, their performance is often constrained by inefficient hyperparameter tuning, susceptibility to local optima, and suboptimal feature extraction. This study addresses these limitations by proposing an intelligent optimization-driven crack detection framework.

Limitations of Conventional Deep Learning-Based Crack Detection

Traditional deep learning models rely heavily on manual or heuristic-based hyperparameter selection, which can lead to unstable training outcomes and reduced generalization performance. Moreover, commonly used optimization techniques may become trapped in local optima, resulting in inaccurate crack localization and increased false positive rates, particularly when dealing with complex backgrounds and diverse infrastructure materials.

Multi-Strategy Enhanced Dung Beetle Optimizer (MSEDBO)

The proposed framework integrates a Multi-Strategy Enhanced Dung Beetle Optimizer (MSEDBO) to systematically optimize critical parameters within the crack detection pipeline. MSEDBO incorporates Latin Hypercube Sampling with elite population initialization, an improved sigmoid-based nonlinear control factor, sine–cosine algorithm integration, and multi-population mutation strategies. These enhancements collectively strengthen global exploration and local exploitation capabilities.

Integration with Deep Learning Models

By embedding MSEDBO into deep learning-based crack detection models, the framework enables adaptive optimization of network parameters and feature extraction processes. This synergy improves convergence behavior, enhances robustness against local optima, and ensures efficient learning across varying crack patterns and surface conditions in civil infrastructure.

Experimental Validation and Benchmark Datasets

The proposed approach was validated using multiple benchmark datasets, including CrackTree200, CFD, GAPs, and SDNET2018, covering a wide range of materials such as concrete pavements, asphalt roads, and bridge surfaces. Comparative experiments demonstrate that the MSEDBO-optimized framework consistently outperforms conventional optimization algorithms and baseline deep learning models.

Performance Gains and Practical Implications

Results show significant improvements, including an 8.7% increase in detection accuracy, a 12.3% improvement in precision, and a 15.6% reduction in false positive rates. The framework maintains computational efficiency while effectively avoiding local optima, making it well suited for real-world deployment. This research advances intelligent infrastructure monitoring by providing a robust optimization strategy to enhance the reliability and accuracy of automated crack detection systems.

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#EngineeringOptimization
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