Cracks are a prevalent disease on pavement concrete materials. Timely assessment and repair of concrete materials can significantly extend their service life. However, accurate segmentation has always been difficult due to their random distribution, tortuous geometry, and varying degrees of severity. To address these challenges, a Multi-stage Supervised Dual-encoder network for Crack segmentation on pavement concrete (MSDCrack) was proposed based on an encoder–decoder architecture. In this network, attention collapse is mitigated through the addition of self-attention pooling. Furthermore, a feature fusion module was designed to address differences in encoding characteristics across branches. Additionally, a multi-stage supervision strategy was implemented to enhance the network’s predictive performance. Comparative experiments demonstrated that MSDCrack achieved the highest Dice coefficient, F1-score, and IoU on multiple datasets, with F1-score and IoU surpassing other state-of-the-art segmentation networks by over 3.1% and 2.89%, respectively, in generalization performance.

中文翻译：

---

双编码器网络，用于路面混凝土裂缝分割，具有多级监控功能


裂缝是路面混凝土材料上普遍存在的疾病。及时评估和修复混凝土材料可以显着延长其使用寿命。然而，由于其随机分布、曲折的几何形状和不同程度的严重性，准确分割一直很困难。为了应对这些挑战，提出了一种基于编码器-解码器架构的用于路面混凝土裂缝分割的多级监督双编码器网络 （MSDCrack）。在这个网络中，通过添加自我注意力池来缓解注意力崩溃。此外，还设计了一个特征融合模块来解决跨分支编码特征的差异。此外，还实施了多阶段监督策略，以提高网络的预测性能。比较实验表明，MSDCrack 在多个数据集上实现了最高的 Dice 系数、 F1 分数和 IoU，F1 分数和 IoU 在泛化性能方面分别比其他最先进的分割网络高出 3.1% 和 2.89% 以上。