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DCP-TransUNet:在道路上进行裂细分的方法
Yunqing Liu1, Xu Du1, Weiguang Li2
1School of Electronic Information and Engineering, Changchun University of Science of Technology, Changchun 130118, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究介绍了DCP-TransUNet,这是一种用于准确细分水泥路面裂的新型深度学习模型. 该模型增强了裂检测连续性和功能学习,以改善道路基础设施安全.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 裂是水泥路面的主要困扰,影响结构完整性和交通安全.
- 准确的裂检测对于有效的路面维护和管理至关重要.
研究的目的:
- 引入DCP-TransUNet,这是一个用于增强水泥路面裂细分的深度学习模型.
- 提高裂纹提取的连续性和准确性,特别是在复杂的条件下.
主要方法:
- 开发了DCP-TransUNet,其中包含DSE-CNN和CLMA-Transformer块的混合编码器.
- 集成了一个PPA瓶模块,以捕捉具有歧视性的特征,以挑战裂图像.
主要成果:
- 在公开数据集上实现了高性能 (mIoU 79.12%,回忆率 87.96%,F1 87.06%,精度 86.21%).
- 在私人数据集上表现出强的结果 (mIoU 68.83%,回忆 74.42%,F1 77.57%,精度 81.67%).
- 在裂细分精度和有效性方面表现优于其他模型.
结论:
- DCP-TransUNet有效地分割了水泥路面上的裂.
- 该模型的架构增强了功能学习和解释性,用于复杂的裂检测.
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