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.
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.
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.
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
Looking to attend an AI event or two this year? Below ... Here are the top 22 machine learning conferences in 2020: ... Start Date: June 10th, 2020 ... Join more than 400 other data-heads in 2020 and propel your career forward. ... They feature 30+ data science sessions crafted to bring specialists in different ...
Create a Tf Lite model using transfer learning on a pre-trained Tensorflow model, optimize it, and run inferences. In this article, you will learn to use a pre-trained model, apply transfer learning, convert the model to TF Lite, apply optimization, and make inferences from the TFLite model.
Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.
Top Deep Learning Frameworks in 2020: PyTorch vs TensorFlow. This article outlines five factors to help you compare these two major deep learning frameworks; PyTorch and TensorFlow.
This full course introduces the concept of client-side artificial neural networks. We will learn how to deploy and run models along with full deep learning applications in the browser! To implement this cool capability, we’ll be using TensorFlow.js (TFJS), TensorFlow’s JavaScript library.