應用深度學習技術輔助橋梁裂縫辨識

Application of Deep Learning Technology to Assist Bridge Crack Identification

王姿樺(Zi-Hua Wang) ； 高書屏(Szu-Pyng Kao) ； 林志憲(Jhih-Sian Lin)

《航測及遙測學刊》 27卷4期 (2022/12) Pp. 247-257

https://doi.org/10.6574/JPRS.202212_27(4).0004

摘要

臺灣橋梁約有兩萬九千座且橋齡30年以上橋梁佔總數31%，傳統橋梁檢測方式容易使判斷結果過於主觀、耗時、高成本且使檢測人員暴露於危險當中，因此藉由深度學習取代傳統檢測並使用公開裂縫資料集及自行拍攝橋梁裂縫資料，選用Faster-RCNN模型搭配ResNet 50為骨幹的卷積神經網路做為裂縫辨識的方法。研究結果證實相較於傳統橋梁檢測方式之檢測效率、安全性及靈活度也相對提升，研究成果對於裂縫辨識平均精度可達到80.7%、召回率可達77%可成功檢測出橋梁受損區域，且測試影像中有87.76%影像能夠完全預測裂縫位置，另針對具干擾辨識的裂縫影像也僅有12.24%影像有誤判情形。

關鍵字

橋梁檢測 ； 深度學習 ； Faster-RCNN ； 卷積神經網路 ； 裂縫辨識

並列摘要

There are about 29,000 bridges in Taiwan and 31% of the bridges are over 30 years old. Traditional bridge inspection methods are too subjective, time-consuming, costly and expose inspectors to danger. Therefore, deep learning was used to replace the traditional inspection method and the Faster-RCNN model with ResNet 50 as the backbone of the convolutional neural network was chosen as the crack identification method using public crack dataset and self-photographed bridge crack data. The results of the study confirmed that the detection efficiency, safety and flexibility were relatively improved compared with the traditional bridge detection method. The average accuracy of the research results for crack identification reached 80.7% and the recall rate reached 77% to successfully detect the damaged area of the bridge. In addition, 87.76% of the test images were able to predict the crack location completely, and only 12.24% of the crack images with interference recognition were misidentified.

並列關鍵字

Bridge Inspection ； Deep Learning ； Faster-RCNN ； Neural Network ； Crack Identification

參考文獻

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