Pothole recognition using convolution neural networks and transfer learning

Chayadevi Senigalakuruba, Suraj Pabba


Potholes have been and still are a huge problem for every walk of life. There are many deaths and accidents reported daily due to that very problem. For that reason, pothole recognition comes into the picture. To maintain and preserve a road, it is vital to detect potholes. It also helps in the prevention of accidents. Roads play an important part in day-to-day transportation for every person around the world. But the quality of roads decreases drastically due to the way of usage and aging. The existing methods take much time and manpower to repair the damaged areas. The entire process is slowing down just because an expert team is checking whether there is a pothole at the reported location or not. So, if we automate the process of detection of potholes from a set of images reported from a particular location and appropriately alerting the authorities with the amount of damage, the process speeds up exponentially. We must solve the major problem of pothole recognition by using machine learning algorithms. This paper will discuss a convolution neural network-based and a transfer learning-based solution for pothole recognition.


Convolutional neural network; Deep learning; Inception V3; Pothole recognition; Transfer learning

Full Text:


DOI: http://doi.org/10.11591/ijai.v12.i3.pp1204-1209


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

View IJAI Stats