AnimalG

Inspiration

As a team of engineers and wildlife enthusiasts, we were deeply troubled by the frequent incidents of railway accidents caused by animals on the tracks. These accidents not only posed risks to human lives but also endangered wildlife populations.

What it does

AnimalG is a system designed to keep railway tracks safe from animals. It constantly watches for animals on the tracks and, if it finds any, it makes loud noises to scare them away. If the animals don't leave, the system alerts railway authorities and nearby wildlife centers to take action.

How we built it

AnimalG utilizes a combination of TensorFlow for real-time animal detection, TypeScript for backend logic, React for the user interface, and Convex database and file storage for efficient data management. The system continuously monitors railway tracks for the presence of animals, emits noise to deter them from staying on the tracks, and sends notifications to relevant authorities if necessary.

Challenges we ran into

One of the main challenges we faced was training the TensorFlow models for accurate animal detection in real-time. This required extensive experimentation with various datasets and model architectures to achieve the desired level of accuracy. Additionally, integrating the different components of the system posed technical challenges, especially in ensuring seamless communication between the detection module, monitoring interface, and notification system.

Accomplishments that we're proud of

We are proud to have developed a comprehensive solution that addresses the complex issue of railway safety in the presence of wildlife. Our system not only detects animals on the tracks but also takes proactive measures to mitigate potential dangers, ultimately contributing to the prevention of accidents and the conservation of wildlife populations.

What we learned

Throughout the development process, we gained valuable insights into machine learning techniques, frontend and backend development, and database management. We also learned the importance of interdisciplinary collaboration in tackling real-world challenges, as our team comprised individuals with diverse expertise in engineering, conservation, and technology.

What's next for AnimalG

Moving forward, we plan to further enhance the capabilities of AnimalG by incorporating advanced features such as predictive analytics for animal behavior, integration with drones for aerial monitoring, and collaboration with wildlife conservation organizations to expand the reach of our solution.

Built With

• javascript
• nextjs
• react
• tailwind
• tensorflow