Bilgisayarlı Görü Tabanlı Yol Kaplaması Tehlikeleri Tespiti için Faster R-CNN Yapısı

Akıllı şehirler tüm yönüyle kontrol altına alınabilir ve kontrol edilebilir geri bildirimleri olması planlanan yapıya sahip olması istenmektedir. Karayolunda otomobil, otobüs gibi taşıtların ulaşımını sağlayan yollarda kaplama malzemesi olarak genellikle asfalt kullanılmaktadır. Asfalt malzemesi hava koşulları, yoğun araç geçişi gibi sebeplerden deforme olmaktadır. Akıllı şehir yapısında buna benzer deformelerin ilgili birime iletilmesi gerekmektedir. Bu makalede görüntü işleme yöntemleri ve derin öğrenme tekniği ile bir bölgeden elde edilen veri seti seçilerek asfalt üzerinde bozulmalar tespit edilmeye çalışılmıştır. Bir otomobile yerleştirilen aksiyon kamerası ile çeşitli bozulmaların olduğu ve herhangi bir bozulmanın olmadığı toplamda 4315 adet asfalt görüntüsü veri seti olarak kullanılmıştır. Piksel tabanlı çalışan Daha Hızlı Bölge Tabanlı Evrişimli Sinir Ağı kullanılarak veri seti sınıflandırılmıştır. Sınıflandırma sonucunda elde edilen performans sonucunun anlamlandırılabilir olması için doğruluk, kesinlik ve duyarlılık değerleri kullanılmıştır. Önerilen bu yöntem ileortalama doğruluk oranı %93.2 elde edilmiştir. Bu sonuçlar ile akıllı şehir yapılarında asfalt bozulmaları için otomatik olarak tespit yapabilen bir yaklaşım geliştirilmiştir.

Anahtar Kelimeler:

Derin Öğrenme, Faster R-CNN, Görüntü İşleme, Kaplama Tehlikesi Tespiti

Faster R-CNN Structure for Computer Vision-based Road Pavement Distress Detection

Smart cities can be controlled in all aspects and it is desired to have a structure that is planned to have controllable feedback. Asphalt is generally used as pavement material on roads that provide transportation of vehicles such as cars and buses on the highway. Asphalt material is deformed due to weather conditions, heavy vehicle passage. In the smart city structure, similar deformations should be reported to the relevant unit. In this article, it was tried to determine the deteriorations on the asphalt by selecting the data set obtained from a region with image processing methods and deep learning technique. With the action camera placed in an automobile, a total of 4315 asphalt images with various distortions and without any deterioration were used as dataset. The dataset was classified using a pixel-based Faster Region-based Convolutional Neural Network. Accuracy, precision and sensitivity values were used to make the performance result obtained as a result of classification meaningful. With this proposed method, the average accuracy rate was 93.2%. With these results, an approach that can automatically detect asphalt deterioration in smart city structures has been developed.

Keywords:

Deep Learning, Faster R-CNN, Image Processing, Pavement Distress Detection,

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