Hybrid Neural Network-Based Road Damage Detection Using CNN-RNN and CNN-MLP Models

Abstract

Currently, there are many applications of image processing in various fields. One of them is the recognition of paved road images. Detection through images helps in handling infrastructure development roads. With the advancement of technology, especially in the field of deep learning, the process of detecting road damage can be done automatically and more efficiently. The road damage detection system can be integrated into the smart city system to monitor infrastructure conditions in real time. This study will use a combined deep learning algorithm between Convolutional Neural Network- Recurrent Neural Network (CNN-RNN) and as a comparison using Convolutional Neural Network- MultiLayer Perceptrons (CNN-MLP). The study aims to analyze the accuracy of using the CNN-RNN and CNN-MLP algorithms for detecting paved roads that have categories of undamaged roads, damaged roads, and damaged roads with holes. The detection of paved roads has complex details so an algorithm that has good performance with high accuracy is needed. The results of the study showed that the CNN-RNN hybrid had a better accuracy of 96.59 percent than the CNN-MLP hybrid model of 95.9 percent.