Challenge background
The road becomes rough, bumpy, and dangerous for cars as a result of several driver violations, such as drifting, using expired tires, and driving at a fast speed. Heavy traffic on roads causes their surfaces to degrade every day which will affect the comfort of the driver as well as economic efficiency. Employing machine learning to research and evaluate many kinds of road issues and automatically spot any abnormalities on the road is an easier and more time-consuming way.
The UAE Vision 2021 National Agenda wants the country to have the best safety record in the entire world. As a result, it aims to improve its citizens' sense of security and take the lead in the fields of safety on the roads, disaster preparedness, and security. The National Agenda also emphasizes the significance of having a just and responsive legal system that protects the rights of people and businesses and makes the UAE's judicial system one of the most effective in the world. Furthermore, the UAE’s federal traffic law was changed on July 1, 2017. As part of Vision 2021, the new regulations seek to better secure the lives of road users by lowering the number of road deaths from 6 per 100,000 to 3 per 100,000.
UAE’s government conducts routine inspections to guarantee the security of all users of the roads. Such a process takes a long time and requires a lot of work. The procedure of road inspection must therefore be automated.
Project timeline
- 1
Week 1
W1.1 Research W1.2 Data Collection W1.3 Data pre-processing
- 2
Week 2
W2.1 Data pre-processing/ W2.2 Prepare/ Explore ML approaches
- 3
Week 3
W3.1 Explore the pre-trained network and ML models W3.2 CNN training and validation 3.3 Performing transfer learning
- 4
Week 4
4.1 System testing and accuracy reporting 4.2 Building Dashboard to visualize the output/ Deployment 4.3 Generate final report and recommendation/ Evaluate Model accuracy
- 5
Week 5
4.1 System testing and accuracy reporting 4.2 Building Dashboard to visualize the output/ Deployment 4.3 Generate final report and recommendation/ Evaluate Model accuracy
What you'll learn
During this project, participants will be mainly able to: - Perform data collection and pre-processing for road images - Investigate the current pre-trained convolutional neural network, the Deep Learning Toolbox, and transfer learning - Apply several machine learning models to classify road images - Build a Dashboard to visualize detected road defects.