城市综合管道画廊表面裂的语义细分,基于全球关注
Yuan Zhou1, Zhiyu Yang2, Xiaofeng Bai1
1School of Instrument Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
一个新的全球关注细分网络 (GA-SegNet) 改善了城市管道画廊的裂检测. 这种方法提高了在具有挑战性的地下环境中的小裂的细分精度.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 城市地下管道画廊含有小,模糊的裂.
- 低亮度和阴影阻碍了在图像中的裂检测.
- 现有的语义细分方法在详细的裂纹特征上扎.
研究的目的:
- 开发一种有效的方法来对城市地下管道画廊中的小裂进行细分.
- 为了解决目前的语义细分方法在检测细裂纹细节方面的局限性.
主要方法:
- 提出了一个全球注意力细分网络 (GA-SegNet),结合全球注意力机制.
- 在编码器中使用剩余可分离的卷积注意模型进行多尺度特征提取.
- 在解码器中使用全局注意力升级样本模型 (GAM) 来链接浅层和深层特征.
- 实现了平衡损失功能,以优先考虑破裂像素而不是背景像素.
主要成果:
- 与经典模型相比,GA-SegNet表现出优越的细分性能.
- 该网络在裂纹细分任务中实现了更高的准确性和效率.
- 观察到对小裂和局部边缘特征的注意力增加.
结论:
- GA-SegNet有效地对城市地下管道画廊中的小裂进行细分.
- 拟议的网络为裂检测提供了更高的准确性和效率.
- 全球注意力机制对于在具有挑战性的图像条件下细分微妙特征至关重要.
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