Classification of Asphalt Road Damage Based on Images Using the Convolutional Neural Network (CNN) Method

M. Rivan  Padila; Arie Qurania; Mulyati Mulyati

doi:10.33365/teknoinfo.v20i1.771

M. Rivan Padila Universitas Pakuan
Arie Qurania Universitas Pakuan
Mulyati Mulyati Universitas Pakuan

DOI: https://doi.org/10.33365/teknoinfo.v20i1.771

Keywords: Artificial Intelligence, Convolution Neural Network (CNN), Google Street View Map, Road Damage, YOLOv5

Abstract

Damage to roads can cause inconvenience in driving and can even lead to accidents. Some of the damages that are often found on the road network are such as fine cracks, alligator skin cracks, potholes, asphalt grain release and others. The damage needs preventive handling because it is the main infrastructure in land transportation that is used every day plus areas with very high rainfall such as Indonesia, Damage to the road surface can occur more quickly. One method in artificial intelligence that can be used in identifying damaged roads is Convolutional Neural Networks (CNN). This method is capable of self-learning for object recognition, object extraction and classification and can be applied to high image resolution. The Citra data is taken from the results of google street view mapping with the application of the CNN model using YOLOv5, which is expected to be able to classify images specifically more effectively, objectively and safely in road maintenance efforts later. This research aims to classify image-based asphalt road damage using the Convolution Neural Network (CNN) method. The stages of this research consist of Data Selection, Preprocessing, Data Transformation, Data Mining and Pattern Evaluation using confusion matrix. The results obtained F1 score model of 73.5%, the value of mean Average Precision (mAP) of 75%, this shows that this model is able to classify fairly against all categories of data used.

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