Inspiration
Imagine a world where every corner of Africa and its local regions thrives with inclusive and sustainable industrialization. Picture small-scale enterprises empowered by access to financial services, seamlessly integrated into global value chains. Envision a future where scientific research and technological advancements uplift industrial sectors, fostering innovation and prosperity. Envision resilient infrastructure supporting economic growth, powered by domestic technology development and innovation. Finally, visualize a connected continent, where information and communications technology is universally accessible and affordable.
In this vision of progress, safety and security are paramount. As we strive for sustainable development, ensuring the safety of our communities is crucial. That's why, inspired by the targets of this hackathon, we have developed a project focused on human detection using YOLOv3. This technology can be a powerful tool for enhancing security and safety, aligning perfectly with the sustainable development goals outlined for this hackathon.
By utilizing YOLOv3 for human detection, we aim to contribute to the creation of safer environments, supporting the inclusive and sustainable industrialization of Africa and its local regions. Our project aligns with the targets of this hackathon by providing a solution that can enhance security, promote technological innovation, and contribute to the overall development and prosperity of the continent.
What it does
Our project, named "SecureZone," utilizes the YOLOv3 model for real-time human detection in industrial and urban areas. The system continuously monitors a live video feed and identifies human presence with high accuracy. When a human is detected for the first time, the system sends an immediate SMS notification using Twilio to alert relevant authorities or personnel. The project aims to enhance safety and security in various environments, including factories, warehouses, and urban public spaces. By providing real-time alerts, it enables quick response to potential security threats or safety issues, contributing to the goal of building resilient infrastructure and promoting inclusive and sustainable industrialization.
How we built it
Technology Stack Programming Languages: Python Libraries/Frameworks: OpenCV, Twilio API
Steps Taken
- Data Collection: We gathered a dataset of images and videos containing various human poses and scenarios for training the YOLOv3 model.
- Model Training: We trained the YOLOv3 model using the collected dataset to detect humans in real-time.
- Integration with OpenCV: We integrated the trained model with OpenCV to process live video streams and detect humans.
- SMS Notification System: We implemented the Twilio API to send SMS notifications when a human is detected in a restricted area.
Challenges we ran into
- Hardware Limitations: Optimizing the YOLOv3 model for real-time performance on our hardware was challenging and required tuning of parameters.
- Integration Complexity: Integrating the Twilio API for SMS notifications required understanding of API authentication and message formatting. ## Accomplishments that we're proud of
- Successfully implemented real-time human detection using the YOLOv3 model.
- Integrated Twilio for SMS notifications to alert authorities of human presence.
What we learned
During the hackathon, We learned how to integrate YOLOv3 for object detection, specifically for detecting humans in real-time. We also learned about the importance of sustainable development and how technology can contribute to achieving the SDGs.
What's next for SecureWatch: Enhancing Security and Safety
- Implementing a more sophisticated notification system with additional context (e.g., location, time) for better decision-making by authorities.
- Enhancing the human detection model with more advanced features for improved accuracy and performance.
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