Deep learning approaches for autonomous crack detection in concrete wall, brick deck and pavement

Abstract

Detecting cracks is vital for inspecting and maintaining concrete structures, enabling early intervention and preventing potential damage. The advent of computer vision and image processing in civil engineering has ushered in deep learning-based semi-automatic/automatic techniques, replacing traditional visual inspections. These methods, driven by autonomous diagnosis, have applications across various sectors, fostering rapid progress in civil engineering. In this study, we present an approach that combines vision transformers and convolutional neural networks (CNN) for autonomously diagnosing cracks in bridges, roads, and walls. Performance enhancement was achieved through transfer learning, data augmentation, and optimized hyperparameters, utilizing popular CNN and ViT architectures. The proposed method was tested on the SDNET2018 dataset, comprising over 56,000 images. Experimental results demonstrated the approach's effectiveness, achieving high accuracy in detecting road cracks at 96.41%, wall cracks at 92.76%, and bridge cracks at 92.81%. These findings highlight the promising potential of deep learning in this field.

Keywords

• Crack detection
• structural cracks
• deep learning
• image processing
• CNN

Beton duvar, köprü tabliyesi ve kaldırım yapılarında otonom çatlak tespiti için derin öğrenme yaklaşımları

Öz

Çatlakların tespiti, beton yapıların incelenmesi ve bakımı, erken müdahalenin sağlanması ve olası hasarların önlenmesi açısından hayati öneme sahiptir. İnşaat mühendisliğinde bilgisayarlı görme ve görüntü işlemenin ortaya çıkışı, geleneksel görsel incelemelerin yerini alarak derin öğrenmeye dayalı yarı otomatik/otomatik tekniklerin yolunu açtı. Otonom teşhisle yönlendirilen bu yöntemlerin çeşitli sektörlerde uygulamaları vardır ve inşaat mühendisliğinde hızlı ilerlemeyi teşvik eder. Bu çalışmada köprüler, yollar ve duvarlardaki çatlakları bağımsız olarak teşhis etmek için görüş transformatörlerini ve evrişimli sinir ağlarını (CNN) birleştiren bir yaklaşım sunulmuştur. Popüler CNN ve ViT mimarileri kullanılarak transfer öğrenimi, veri artırma ve optimize edilmiş hiper parametreler yoluyla performans iyileştirmesi sağlandı. Önerilen yöntem, 56.000'den fazla görüntü içeren SDNET2018 veri kümesi üzerinde test edildi. Deneysel sonuçlar, yaklaşımın etkinliğini gösterdi; yol çatlaklarını %96,41, duvar çatlaklarını %92,76 ve köprü çatlaklarını %92,81 tespit etmede yüksek doğruluk elde etti. Bu bulgular, bu alanda derin öğrenmenin umut verici potansiyelini ortaya çıkarmaktadır.

Anahtar Kelimeler

• Çatlak tespiti
• yapısal çatlaklar
• derin öğrenme
• görüntü işleme
• CNN

Kaynakça

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