Road crack detection is essential for intelligent infrastructure maintenance in smart cities. To reduce reliance on costly pixel-level annotations, we propose WP-CrackNet, an end-to-end weakly-supervised method that trains with only image-level labels for pixel-wise crack detection. WP-CrackNet integrates three components: a classifier generating class activation maps (CAMs), a reconstructor measuring feature inferability, and a detector producing pixel-wise road crack detection results. During training, the classifier and reconstructor alternate in adversarial learning to encourage crack CAMs to cover complete crack regions, while the detector learns from pseudo labels derived from post-processed crack CAMs. This mutual feedback among the three components improves learning stability and detection accuracy. To further boost detection performance, we design a path-aware attention module (PAAM) that fuses high-level semantics from the classifier with low-level structural cues from the reconstructor by modeling spatial and channel-wise dependencies. Additionally, a center-enhanced CAM consistency module (CECCM) is proposed to refine crack CAMs using center Gaussian weighting and consistency constraints, enabling better pseudo-label generation. We create three image-level datasets and extensive experiments show that WP-CrackNet achieves comparable results to supervised methods and outperforms existing weakly-supervised methods, significantly advancing scalable road inspection. The source code package and datasets are available at https://mias.group/WP-CrackNet/.

翻译：道路裂缝检测对于智慧城市中的智能基础设施维护至关重要。为减少对昂贵像素级标注的依赖，我们提出WP-CrackNet，一种端到端的弱监督方法，仅使用图像级标签进行像素级裂缝检测训练。WP-CrackNet集成三个组件：生成类别激活图（CAMs）的分类器、衡量特征可推断性的重构器，以及输出像素级道路裂缝检测结果的检测器。训练过程中，分类器与重构器通过对抗学习交替协作，促使裂缝CAMs覆盖完整的裂缝区域，而检测器则从后处理裂缝CAMs生成的伪标签中学习。三个组件间的相互反馈提升了学习稳定性与检测精度。为进一步提升检测性能，我们设计了路径感知注意力模块（PAAM），通过建模空间与通道维度依赖关系，融合分类器的高级语义信息与重构器的低级结构线索。此外，提出中心增强CAM一致性模块（CECCM），利用中心高斯加权与一致性约束优化裂缝CAMs，从而生成更优质的伪标签。我们构建了三个图像级数据集，大量实验表明WP-CrackNet取得了与全监督方法相当的结果，并优于现有弱监督方法，显著推动了可扩展道路检测的发展。源代码包与数据集公开于https://mias.group/WP-CrackNet/。