Optimal Fuzzy Wavelet Neural Network based Road Damage Detection

Floods are one of the most severe and frequent natural calamities. It causes enormous economic damage even leads to higher mortality rates. Studies on detection roads using artificial intelligence (AI) methods gained more attention currently, especially in development smart cities. Therefore, this study designs an optimal Fuzzy Wavelet Neural Network based Road Damage Detection (OFWNN-RDD) technique for Flooding Management. The OFWNN-RDD aims exploit remote sensing images classify different kinds roads. For noise removal process, utilizes Gabor filtering (GF) technique. In addition, uses DenseNet121 model feature vector generation with modified barnacles mating optimization (MBMO) hyperparameter optimizer. Finally, FWNN image classification approach is used road detection. simulation values exhibit supremacy over other models improved accuracy 98.56%.