An automated pothole detection via transfer learning
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Potholes on the roads can cause many problems in traffic. They can cause malfunctions of vehicles, deterioration of suspension systems, additional repairs, and traffic accidents. It is very important to detect potholes quickly and with low costs for the maintenance and rehabilitation of roads. This shows that there is a need for automatic systems that can detect structural problems that may occur on the roads quickly and accurately. In this study, DenseNet121 architecture, which is a deep learning-based method, is proposed for detecting potholes in roads. With the proposed approach, it is aimed to determine whether there are potholes in the road images in the dataset. In this study, potholes on the road were detected with 99.3% accuracy using the DenseNet121 network. This success is quite high when compared to similar studies in the literature. At the same time, this dataset was run and compared with ResNet50, InceptionV3, VGG19 and InceptionResnetV2 models with the same parameters. Among these models, the highest accuracy was obtained with DenseNet121.
Açıklama
International Conference on Decision Aid Sciences and Applications (DASA) -- MAR 23-25, 2022 -- Chiangrai, THAILAND
Anahtar Kelimeler
Pothole Detection, Deep Learning, Deep Neural Networks, Transfer Learning
Kaynak
2022 International Conference on Decision Aid Sciences and Applications (Dasa)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
