Applications of Deep Learning Models for Detecting the Road Damages and Obstacles Using Multiple Images

Road damage has become a common issue for accidents all over the world. This research study has worked on the topic of detecting road surface damage by using various image processing techniques. Many of the studies that are available are only for detecting the presence or absence of damage. However, in a real-time scenario, to take effective action, government agencies must consider the type of damage when ensuring the repair of the damage. This study has discovered three solutions to the aforementioned issues. First, a large-scale dataset is gathered. This dataset comprises over 7000 road surface damage images. These images were captured using different illuminance and weather conditions. The image detection process using a convolution neural network is used to train the road crack detection with our dataset with accuracy and speed. Finally, this study conclude that the images have been classified into three categories by applying the image detection classification to the images collected.