The occurrence of pavement cracks poses a significant potential threat to road safety, thus the rapid and accurate acquisition of pavement crack information is of paramount importance. Deep learning methods have the capability to offer precise and automated crack detection solutions based on crack images. However, the slow detection speed and huge model size in high‐accuracy models are still the main challenges required to be addressed. Therefore, this research presents a lightweight feature attention fusion network for pavement crack segmentation. This structure employs FasterNet as the backbone network, ensuring performance while reducing model inference time and memory overhead. Additionally, the receptive field block is incorporated to simulate human visual perception, enhancing the network's feature extraction capability. Ultimately, our approach employs the feature fusion module (FFM) to effectively combine decoder outputs with encoder's low‐level features using weight vectors. Experimental results on public crack datasets, namely, CFD, CRACK500, and DeepCrack, demonstrate that compared to other semantic segmentation algorithms, the proposed method achieves both accurate and comprehensive pavement crack extraction while ensuring speed.

中文翻译：

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用于路面裂缝分割的轻量级特征注意融合网络

路面裂缝的发生对道路安全构成重大潜在威胁，因此快速、准确地获取路面裂缝信息至关重要。深度学习方法能够根据裂纹图像提供精确且自动化的裂纹检测解决方案。然而，高精度模型中检测速度慢和模型尺寸巨大仍然是需要解决的主要挑战。因此，本研究提出了一种用于路面裂缝分割的轻量级特征注意融合网络。该结构采用FasterNet作为主干网络，保证性能的同时减少模型推理时间和内存开销。此外，还结合了感受野模块来模拟人类视觉感知，增强了网络的特征提取能力。最终，我们的方法采用特征融合模块（FFM）使用权重向量有效地将解码器输出与编码器的低级特征组合起来。在公共裂缝数据集CFD​​、CRACK500和DeepCrack上的实验结果表明，与其他语义分割算法相比，该方法在保证速度的同时实现了准确、全面的路面裂缝提取。