- 🔬 Data Science
- 🥠 Deep Learning and Object Detection
Table of Contents¶
- Introduction and objective
- Necessary imports
- Prepare data that will be used for training
- Model training
- Model inference
Deterioration of road surface due to factors including vehicle overloading, poor construction quality, over ageing, natural disasters and other climatic conditions may lead to road pavement failure. This may result in traffic slowness causing jams and vehicle damage due to cracks. This also causes problems for civic authorities who are in need to accurately identify these cracks and do the repair work. If these cracks are not repaired at early stages, cost of repair gradually increases causing unnecessary burden on exchequer.
Traditionally, inspection of road surface is done by humans either by visually observing it or by using sophisticated machines which are expensive too. The manual approach to detect damage is not just time consuming but is also ineffective since detection of such damages requires consistent help from subject matter experts who have the ability to identify and differentiate different types of pavement failures. Artificial Intelligence supported by Deep Learning comes to the rescue. Deep learning integrated with ArcGIS plays a crucial role by automating the process.
In this notebook, We use a great labeled dataset of asphalt distress images from the 2018 IEEE Bigdata Cup Challenge in order to train our model to detect as well as to classify type of road cracks. The training and test data consists of 9,053 photographs, collected from smartphone cameras, hand labeled with the presence or absence of 8 road damage categories .
The table below shows sample images of the dataset corresponding to each of the 8 categories of damage type.
|Class Name||Class Description||Image|
|D00||Liner, crack, longitudinal, wheel mark part|
|D01||Liner crack, longitudinal, construction joint part|
|D10||Liner crack, lateral, equal interval|
|D11||Liner crack, lateral, construction, joint part|