编码器
干扰(通信)
计算机科学
分割
交叉口(航空)
人工智能
模式识别(心理学)
工程类
计算机网络
操作系统
频道(广播)
航空航天工程
作者
Jian Deng,Ye Lu,Vincent CS Lee
出处
期刊:Measurement
[Elsevier]
日期:2023-07-01
卷期号:216: 112892-112892
被引量:3
标识
DOI:10.1016/j.measurement.2023.112892
摘要
Although many learning-based studies have been conducted to detect cracks, there are still many problems in practice, such as slow inference speed due to a large number of hyperparameters required in network architectures and compromised detection accuracy in different environments. To address these issues, the current study employed a Hybrid Lightweight Encoder-Decoder Network (HLEDNet) as an ad-hoc crack segmentation and measurement system on real-world images captured from various concrete bridges. The proposed HLEDNet model was trained and tested with 3000 annotated images with further extensive data augmentation, which achieved 86.92%, 85.71%, 86.31, and 86.01% in precision, recall, F1 score, and mean intersection over union (mIoU), respectively. A crack measurement module was proposed using combined postprocessing techniques, where the R-squared values of the regression lines in crack length and average crack width are 0.9857 and 0.9925, respectively. Finally, an experimental study was undertaken to convert the crack measuring unit from pixel to millimetre.
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