稳健性(进化)
计算机科学
人工智能
生成对抗网络
自动化
过程(计算)
生成语法
故障检测与隔离
桥(图论)
机器学习
深度学习
块(置换群论)
对抗制
领域(数学)
工程类
机械工程
基因
几何学
操作系统
医学
生物化学
内科学
数学
执行机构
化学
纯数学
作者
FuTao Ni,Zhili He,Shang I Brian Jiang,Weiguo Wang,Jian Zhang
标识
DOI:10.1016/j.aei.2022.101575
摘要
Traditional manual crack detection has been gradually replaced by unmanned aerial vehicles (UAVs) since automation and intelligence became the inevitable trends in routine bridge maintenance. Deep learning-based real-time crack detection is an important link in this automation process. However, due to the limitations of the field of view and airborne computer performance, it is challenging to balance crack detection accuracy and efficiency at the same time. To address this issue, a novel Generative Adversarial Network (GAN)-based strategy is proposed in this paper. Different from the traditional ways, the GAN-based strategy can introduce the morphological difference between the predictions and the manual labels into training process, further improving the network performance while ensuring detection efficiency. Three lightweight networks with different depths are designed based on the Dense Block to analyze the impact of the proposed method. Novel Fault-Tolerance (FT) indexes are proposed to reflect the morphological differences in predictions. Finally, the effectiveness and robustness of the proposed method are verified by the crack detection of highway bridge piers. Results show that the proposed method can effectively improve the detection scores of UAV-captured images under limited network parameters.
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