EfficientCrackNet: A Lightweight Model for Crack Segmentation

Crack detection, particularly from pavement images, presents a formidable challenge in computer vision due to inherent complexities such as intensity inhomogeneity, intricate topologies, low contrast, and noisy backgrounds. Automated crack detection is crucial for maintaining the structural integrity of essential infrastructures, including buildings, pavements, and bridges. Existing lightweight methods often face challenges, including computational inefficiency, complex crack patterns, and difficult backgrounds, leading to inaccurate detection and impracticality for real-world applications. We propose EfficientCrackNet, a lightweight hybrid model combining Convolutional Neural Networks (CNNs) and transformers for precise crack segmentation to address these limitations. EfficientCrackNet integrates depthwise separable convolutions (DSC) layers and MobileViT block to capture global and local features. The model employs an Edge Extraction Method (EEM) for efficient crack edge detection without pretraining and an Ultra-Lightweight Subspace Attention Module (ULSAM) to enhance feature extraction. Extensive experiments on three benchmark datasets, Crack500, DeepCrack, and GAPs384, demonstrate that EfficientCrackNet achieves superior performance compared to existing lightweight models, requiring only 0.26M parameters and 0.483 GFLOPs. The proposed model offers an optimal balance between accuracy and computational efficiency, outperforming state-of-the-art lightweight models and providing a robust and adaptable solution for real-world crack segmentation.