Crack detection, particularly from pavement images, presents a formidable challenge in the domain of computer vision due to several 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. To address these limitations, we propose EfficientCrackNet, a lightweight hybrid model combining Convolutional Neural Networks (CNNs) and transformers for precise crack segmentation. EfficientCrackNet integrates depthwise separable convolutions (DSC) layers and MobileViT block to capture both global and local features. The model employs an Edge Extraction Method (EEM) and for efficient crack edge detection without pretraining, and 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, while requiring only 0.26M parameters, and 0.483 FLOPs (G). 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.

翻译：裂缝检测，尤其是从路面图像中进行检测，由于存在强度不均匀性、复杂拓扑结构、低对比度以及噪声背景等固有复杂性，在计算机视觉领域是一项艰巨挑战。自动化裂缝检测对于维护建筑物、路面和桥梁等重要基础设施的结构完整性至关重要。现有的轻量级方法常面临计算效率低下、裂缝模式复杂以及背景干扰等挑战，导致检测不准确且难以应用于实际场景。为克服这些局限，本文提出EfficientCrackNet——一种结合卷积神经网络（CNN）与Transformer的轻量级混合模型，用于精确的裂缝分割。EfficientCrackNet集成深度可分离卷积（DSC）层与MobileViT模块，以同时捕获全局与局部特征。该模型采用无需预训练的边缘提取方法（EEM）进行高效裂缝边缘检测，并引入超轻量子空间注意力模块（ULSAM）以增强特征提取能力。在Crack500、DeepCrack和GAPs384三个基准数据集上的大量实验表明，EfficientCrackNet仅需0.26M参数和0.483 GFLOPs的计算量，即可超越现有轻量级模型的性能。所提模型在精度与计算效率间实现了最佳平衡，其性能优于当前最先进的轻量级模型，为实际裂缝分割任务提供了鲁棒且适应性强的解决方案。