分割
跳跃式监视
人工智能
最小边界框
熵(时间箭头)
苦恼
计算机科学
深度学习
模式识别(心理学)
图像(数学)
机器学习
生态学
量子力学
生物
物理
作者
Hancheng Zhang,Qian Zhang,Yunfeng Tan,Youhua Xie,Miaocheng Li
标识
DOI:10.1016/j.conbuildmat.2022.129117
摘要
This paper presents efficient and cost-effective methods to identify pavement crack distress and thereby substantially increase pavement strength. Detecting the origin of this distress is the key to restoring pavement performance. To do that, a deep learning method is used to detect cracks based on the weakly supervised instance segmentation (WSIS) framework. A bounding box-level crack image data is preprocessed. Pseudo labels are generated by a region growing algorithm and a GrabCut algorithm. Another important contribution is a new dynamically balanced binary cross-entropy loss function. Results show that the WSIS framework reduces manual marking and has a high recognition accuracy of crack distress.
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