MDCCM: a lightweight multi-scale model for high-accuracy pavement crack detection

Effective crack detection is vital for pavement safety and durability. In recent years, deep learning methods have achieved promising results in automated crack detection. However, advanced large-scale convolutional neural networks (CNNs) often rely on numerous trainable parameters for deep feature extraction, therefore, these models are computationally expensive, the complexity of these advanced models makes them impractical for deployment on small Internet of Things devices. In this study, we introduce a novel model specifically designed for pavement crack detection, named Multi-Scale and Detail-Attention-based Crack Classification Model, we adopts a novel multi-scale dual-branch structure for effective feature extraction, the focus is on improving the model's ability to perceive local and global information at different semantic scales, using a decoupled attention mechanism to achieve more effective focus on key information. In addition, we introduce a Stem Block to reduce the feature representation dimension, making the model more lightweight. We tested our proposed model on two standard datasets, the experimental results indicate that our model achieves a parameter count of only 0.41 M, while maintaining a crack detection accuracy exceeding 99%. Compared to existing CNN models, our model outperforms current methods in terms of both complexity and detection accuracy. These results demonstrate the proposed model offers superior performance for pavement crack detection, making it highly suitable for practical applications.