ResNet Blended Five Dense Layered Deep Fully Connected Neural Network based Road Damage Classification

Operating a vehicle on a deteriorated roadway causes an enormous threat to safety, particularly for self-driving automobiles. Deep learning-based image processing technology has made it possible to study models that assess roadway conditions. Disparity of roadway damage and complicated backgrounds in roadway shots makes it an extremely difficult challenge classifying the road damage. To address this problem, this paper recommends ResNet Blended Five Dense Layered Deep Fully Connected Neural Network (RB-5DFCNN) that classifies the road damage with high efficiency. The KAGGLE Road Damage dataset, which consists of 2074 road snapshots, is used by the proposed RB-5DFCNN model. In order to classify road damage, the RB-5DFCNN model first preprocesses the images before fitting ResNet CNN, which consists of five steps of convolutional block and identity block processing. After the ResNet model completed its task, five dense layers were applied to the DFCNN in order to classify the state of road damage. The classification of road damage condition and the determination of the necessary number of dense layers to identify road damage with high accuracy are the innovative aspects of the proposed RB-5DFCNN. To test the effectiveness of the road damage categorization, the Road Damage dataset was applied to both the proposed RB-5DFCNN model and the current CNN approaches. Python execution on a GTX Geforce Tesla V100 Nvidia graphics card server over 64 and 150 training intervals shows that the suggested RB-5DFCNN model projects an accuracy of 98.77% in comparison to the state-of-the-art CNN models.