Beton çatlakların derin öğrenme tabanlı semantik segmentasyonunda kodlayıcı değişkenlerinin karşılaştırmalı analizi

Abstract

Depremler, seller ve yangınlar gibi doğal afetler akabinde yapılarda ve kentsel altyapıda ciddi hasarlar meydana gelmektedir. Çatlaklar, beton yapılarda meydana gelen hasarların veya bozulmaların en yaygın belirtileri olarak kabul edilmektedir. Dolayısıyla, çatlak kusurlarının erken ve doğru bir şekilde tespit edilmesi, bu tür yapıların güvenliklerinin sağlanması ve hizmet süreleri açısından önem arz etmektedir. Son yıllarda bilgisayarlı görü uygulamalarında önemli bir atılım sergileyen derin öğrenme mimarileri, beton çatlaklarının otomatik olarak tespit ve segmente edilmesinde yaygın olarak kullanılmaya başlanmıştır. Özellikle, genel olarak bir kodlayıcı ve bir kod çözücü bloktan oluşan derin öğrenme tabanlı segmentasyon mimarileri çatlakları uzamsal sınırları ile tespit ederek, kapsamlı yapı sağlığı analizlerini mümkün kılmaktadır. Ancak, evrişimsel filtrede küçük alıcı alan, pooling işleminin neden olduğu bilgi kayıpları ve yetersiz yerel özellik işlenmesi gibi kodlayıcı blok sınırlandırmaları segmentasyon performansını sekteye uğratmaktadır. Bu çalışmada, beton yüzeylerindeki çatlakların segmentasyonu için önerilen DeepLabV3+ mimarisinde kodlayıcı blok için farklı omurga mimarilerinin (ResNet-18, ResNet-50, MobileNetV2, Xception ve Inception-ResNet) etkinlikleri analiz edilmiştir. Farklı omurga mimariler ile sağlanan alçak ve yüksek seviyeli özelliklerin etkinliklerinin test edilmesi için erişime açık Deepcrack ve CrackForest veri setleri kullanılmıştır. Bulgular her iki veri seti için de MobileNetV2 mimarisinin eğitilebilir parametre ve segmentasyon perfromansı açısından en başarılı ağ olduğunu göstermiştir. MobileNetV2 kodlayıcı tabanlı segmentasyon çerçevesi, yaklaşık 6.7 milyon eğitilebilir ağırlık kullanarak her iki veri seti için sırasıyla 0.81 ve 0.70 Dice benzerlik katsayısı (DSC) başarımı elde etmiştir.

Keywords

• Semantik Segmentasyon
• Derin Öğrenme
• Görüntü İşleme
• Çatlak Tespiti
• Çatlak Segmenasyonu
• Yapı Denetimi

Comparative analysis of encoder variants in deep learning-based semantic segmentation of concrete cracks

Abstract

Following natural disasters such as earthquakes, floods, and fires, significant damages manifest in both buildings and urban infrastructure. Cracks are widely recognized as the predominant indicators of damage or deterioration in concrete structures. Hence, the early and accurate detection of crack defects is crucial to ensure structural safety and longer service life. Deep learning architectures, which have made a significant breakthrough in computer vision applications in recent years, have begun to be widely used in the automatic detection and segmentation of concrete cracks. In particular, deep learning-based segmentation architectures, typically comprising an encoder and a decoder part, play a crucial role in conducting thorough structural health analyses by precisely detecting cracks along with their spatial boundaries. However, encoder block limitations such as the small receptive field of convolution kernels, information losses caused by the pooling operation, and insufficient local feature processing can hinder segmentation performance. This study examines the efficacy of various backbone architectures (ResNet-18, ResNet-50, MobileNetV2, Xception, and Inception-ResNet) as employed in the encoder block within the DeepLabV3+ framework, proposed for the segmentation of cracks on concrete surfaces. The effectiveness of low-level and high-level features provided by different backbone architectures in the encoder part was evaluated on open-access DeepCrack and CrackForest datasets. The results revealed that the MobileNetV2 architecture was the most successful network in terms of learnable parameters and segmentation performance for both data sets. The MobileNetV2 encoder-based segmentation framework achieved 0.81 and 0.70 Dice similarity coefficient (DSC) for both datasets, respectively, using approximately 6.7 million learnable weights.

Keywords

• Semantic Segmentation
• Deep Learning
• Image Processing
• Crack Detection
• Crack Segmentation

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