Reducing the Number of Car Accidents in Kosovo Through Machine Learning

Local Chapter Kosovo Chapter

Coordinated by,

Olti Gashi

Status: Ongoing

Project background.

Kosovo, being a youthful nation, offers abundant opportunities for development. Primarily, only a handful of companies are addressing local challenges using AI tools. By collaborating with Omdena, we have the potential to tackle various pressing issues in Kosovo, such as the alarmingly high incidence of car accidents. In the year 2021 alone, there were more than 10,000 accidents, which is significant considering the country’s population of 1.7 million. One possible approach to address this problem would involve constructing a machine learning or deep learning model that can accurately detect the real-time speed of vehicles and, based on license plates, identify individuals who have exceeded the speed limit.

The problem.

Our goal is to address the prevalent issue of high-speed related car accidents in our local community. Through the application of machine learning and deep learning methodologies, we intend to craft a solution capable of real-time vehicle speed assessment and effective license plate detection. This technology will facilitate the identification of speed limit violators, allowing us to take necessary actions in enforcing traffic rules.

Implementing such a solution will have a profound impact on our local community. Firstly, it will significantly contribute to the reduction of car accidents, preventing injuries and saving lives. By addressing the root cause of high-speed incidents, we can create safer roads and enhance the overall well-being of our residents.

Moreover, this initiative will promote responsible driving behavior and increase awareness about the importance of adhering to speed limits. By holding individuals accountable for their actions, we can cultivate a culture of safe driving practices within our community. This, in turn, will lead to a decrease in the number of accidents, resulting in reduced property damage and financial losses for individuals and the community as a whole.

Overall, the implementation of an AI-powered solution to address the issue of high-speed accidents will bring about tangible and long-lasting benefits for our local community, fostering a safer environment for all residents and contributing to the sustainable growth and development of Kosovo.

Project goals.

- Help growth and development of Kosovo. - Refresh and learn many new thing in Leading and AI.

Project plan.

  • Week 1

    1. Task: Project Planning and Research
    – Define the project requirements and objectives
    – Research existing license plate recognition and speed detection models
    – Identify the required datasets and data sources
    – Define the evaluation metrics for the model
    1a. Task: Data Collection
    – Identify and gather license plate and speed-related datasets
    – Clean and preprocess the collected data
    – Ensure the data is labeled or annotated appropriately

  • Week 2

    2. Task: Data Preprocessing
    – Explore and analyze the collected data
    – Perform data augmentation techniques if required
    – Split the data into training, validation, and testing sets

  • Week 3

    3. Task: Model Selection and Training – Choose an appropriate deep learning model architecture for license plate recognition and speed detection – Implement the chosen model using a deep learning framework (e.g., TensorFlow or PyTorch) – Train the model using the training dataset – Evaluate the model’s performance using the validation dataset

  • Week 4

    4-5. Task: Model Optimization and Fine-tuning – Perform model optimization techniques (e.g., hyperparameter tuning) – Fine-tune the model to improve its performance – Continue evaluating the model’s performance on the validation dataset

  • Week 5

    4-5. Task: Model Optimization and Fine-tuning – Perform model optimization techniques (e.g., hyperparameter tuning) – Fine-tune the model to improve its performance – Continue evaluating the model’s performance on the validation dataset

  • Week 6

    6. Task: Model Evaluation and Testing – Evaluate the trained model using the testing dataset – Assess the model’s performance based on the defined evaluation metrics

  • Week 7

    7. Task: Deployment Preparation (Streamlit) – Prepare the model for deployment – Package the model and its dependencies – Create a user-friendly interface for license plate recognition and speed detection

  • Week 8

    8. Task: Model Deployment and Integration – Deploy the model to the desired environment (e.g., cloud server or local machine) – Test the deployed model with real-world data – Integrate the model into the desired application or system

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