Road Crack Detection Using Deep Neural Networks Developed via Cooperative Coevolution with Backpropagation

Öz

Cracks in highway flexible pavements are primarily caused by traffic loads and weather conditions. Detecting these cracks before they expand and conducting necessary maintenance will not only ensure the continuity of road comfort but will also reduce maintenance costs. This study aims to detect cracks on the road in real-time and with high accuracy. In this context, Deep Neural Networks Developed via Cooperative Coevolution with Backpropagation and image processing methods were used together. Moreover, a new data set was obtained by using EdmCrack600, AsphaltCrack, CFD, and CrackSegmentation datasets containing cracked visual data in various numbers and resolutions, and Deep Neural Networks-based learning was performed on this dataset. The accuracy of the model was evaluated with Precision, Recall, and F1-Score using the CFD dataset. As a result of the evaluation, it has been observed that the proposed method can detect cracks on 48 images per second, while it can reach 92.74% Precision, 88.92% Recall, and 89.61% F1-Score success rates.

Anahtar Kelimeler

• Road Crack Detection
• Cooperative Coevolution with Backpropagation
• Deep Learning
• Image Processing
• Flexible Pavement

GERİ YAYILIMLI BİRLİKTE EVRİM İLE İYİLEŞTİRİLMİŞ DERİN SİNİR AĞLARI KULLANILARAK YOL ÇATLAK TESPİTİ

Öz

Karayolu esnek üstyapılarındaki çatlaklar genellikle trafik yükleri ve hava koşullarından kaynaklanır. Bu çatlakların genişlemeden tespit edilip gerekli bakımlarının yapılması, yol konforunun sürekliliğini sağlamanın yanı sıra bakım maliyetlerini de azaltacaktır. Bu çalışma, yoldaki çatlakları gerçek zamanlı ve yüksek doğrulukla tespit etmeyi amaçlamaktadır. Bu bağlamda, Geri Yayımlı Birlikte Evrim yaklaşımıyla İyileştirilmiş Derin Sinir Ağları ve görüntü işleme yöntemleri birlikte kullanılmıştır. Ayrıca, çeşitli sayı ve çözünürlüklerde çatlaklı görsel veriler içeren EdmCrack600, AsphaltCrack, CFD ve CrackSegmentation veri setleri kullanılarak yeni bir veri seti oluşturulmuş ve bu veri seti üzerinde Derin Sinir Ağları tabanlı öğrenme gerçekleştirilmiştir. Modelin doğruluğu, CFD veri seti kullanılarak Kesinlik, Duyarlılık ve F1-Skoru ile değerlendirilmiştir. Değerlendirme sonucunda, önerilen yöntemin saniyede 48 görsel üzerinde çatlak tespit edebildiği ve %92,74 Kesinlik, %88,92 Duyarlılık ve %89,61 F1 Skoru başarı oranlarına ulaştığı gözlemlenmiştir.

Anahtar Kelimeler

• Geri Yayılımlı Birlikte Evrim
• Derin Sinir Ağları
• Çatlak Tespiti
• Görüntü İşleme
• Esnek Üstyapı

Kaynakça

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