Karayollarındaki Asfalt Çatlaklarının Tespiti İçin Yeni Bir Konvolüsyonel Sinir Ağı Tabanlı Yöntem

Year 2022, Volume: 34 Issue: 2, 485 - 494, 30.09.2022

Gürkan Doğan Burhan Ergen

https://doi.org/10.35234/fumbd.1014951

Cited By: 2

Abstract

Karayolu yüzeyindeki asfalt çatlakları en yaygın kaplama hasarlarından biridir. Bu çatlaklar zamanında tespit edilip bakıma alınmazsa büyüyerek trafik güvenliğini ve yoğunluğunu arttıracak boyutlara ulaşmaktadır. Bu nedenle, asfalt çatlaklarının tespitinin hızlı bir şekilde gerçekleştirilmesi çok önemlidir. Geleneksel manuel çatlak tespiti, son derece zaman alıcı, çok maliyetli ve çok çaba gerektirir. Bu nedenle, araştırmacılar odaklarını otomatik asfalt çatlaklarının tespitine yoğunlaştırdılar. Ancak, son yıllarda otomatik çatlak tespiti kapsamlı olarak araştırılmasına rağmen çatlakların farklı yoğunluğa sahip olması ve kaplama ortamının karmaşıklığı nedeniyle hala zor bir görevdir. Bu çalışmada, bu zorluğun üstesinden gelmek için konvolüsyon sinir ağı temelli bir yöntem önerildi. Önerilen yöntem, sınıflandırma ve segmentasyondaki başarısı ve hafifliği bilinen MobileNetv2 ’nin temelde kullandığı konvolüsyon ve ters artıklı blok yapılarını baz alarak geliştirildi. Yapılan deneysel testler sonucunda, önerilen yöntemin performansı literatürdeki diğer yöntemlere göre daha yüksek olduğu görülmektedir. Bu da, otomatik asfalt çatlak tespitinin daha başarılı olması anlamına gelmektedir.

Keywords

Çatlak tespiti, asfalt çatlakları, piksel tabanlı segmentasyon, konvolüsyonel sinir ağları

A New Convolutional Neural Network Based Method for Detection of Asphalt Cracks on Highways

Abstract

Asphalt cracks on the highway surface are one of the most common pavement damage. If these cracks are not detected and taken care of on time, they grow and reach dimensions that will increase traffic safety and density. Therefore, it is very important to carry out the detection of asphalt cracks quickly. Traditional manual crack detection is extremely time consuming, very costly and requires a lot of effort. Therefore, the researchers concentrated their focus on the detection of automatic asphalt cracks. However, although automatic crack detection has been extensively investigated in recent years, it is still a difficult task due to the fact that cracks have different densities and the complexity of the pavement environment. In this study, a convolutional neural network-based method is proposed to overcome this difficulty. The proposed method was developed based on the convolution and inverted residual block structures used by MobileNetv2, which is known for its success and lightweight in classification and segmentation. As a result of the experimental tests, it is seen that the performance of the proposed method is higher than the other methods in the literature. This means that automatic asphalt crack detection is more successful.

Keywords

Crack detection, asphalt cracks, pixel-wise segmentation, convolutional neural networks

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