As a part of self case study on Deep learning, I selected a problem on detecting Road damage for multiple countries which is a major issues in almost every countries around the globe.
In this blog, I will explain you how I approached the problem and solved this problem using YOLOv3 architecture inorder to detect road damages for Indian and Japan roads.
Business Problem:
Road infrastructure plays crucial role in saving lives and economic development of a country, so to reduce the road accidents due to potholes and damaged roads it’s an important task to manage and inspect the roads on a timely basis because roads deteriorate over time considering various factors related to location,age, temperature etc. Visual inspection of roads by engineers is very time consuming given the extensive length of roads or highways. So to come up with an automated AI based solution which can detect the type of damage can help and improve the way of monitoring of road conditions.
So the main agenda of this problem is to analyze how can we utilize the Japanese dataset to detect road damages in other countries by adding that country’s images where AI system has to develop.
Dataset:
For this problem, I collected dataset from this link.
This dataset consists of three zip files one for training data and other two for test data containing Images and xml files with annotations. And for this problem
- train.tar : This file comprising of 26620 images and its respective annotation xml files collected from three different countries i.e Japan, India and Czech , Japan dataset has more number of images as compared to India and Czech generated using smartphones.
- test1.tar : This file comprising of 2282 images except without annotation files as I have to test model performance on this images.
#deep-learning #tensorflow #tensorflow-lite #yolov3
