Crevices Recognition on Asphalt Surfaces using Convolutional Neural Network

Heavy rainfalls leading to floods in cities and villages is a common sight in our country. These situations lead to destruction of roadways and bridges, and often public infrastructure as an aftermath. Inspection of such facilities to assess the damage and identify any potential vulnerability is a tedious process. Some of the cracks/crevices might not be even visible to the naked eye. An automated system which can detect cracks saves money, time and even lives. This will help us improve road safety which is the reason for major accidents. The proposed work uses machine learning concepts to implement such a system which automatically detects the cracks on the roads, bridges and will send an alert to the concerned authorities there by potentially reducing the risk for disaster occurrence. Convolutional Neural Networks (CNN) can be used for the identification of cracks. By integrating the CNN Classifier with the camera, the cracks can be automatically detected in that region and reported.