AvianEye - AI-Powered Drone for Environmental Monitoring
Siemens PAMC Lab Internship Project
AvianEye was developed as part of my internship at Siemens’ prestigious PAMC (Platform and Module Competency) Lab, where I worked at the intersection of embedded systems and artificial intelligence. The drone was designed as a professional-grade environmental monitoring platform, integrating AI-driven bird detection and classification with robust hardware foundations. I contributed extensively to embedded software, PCB design, and implementing communication protocols such as UART, SPI, and I2C. I also developed Convolutional Neural Networks (CNNs) with TensorFlow, tested AI models in real-world conditions, and collaborated closely with Siemens engineers on AI integration into embedded workflows. This project not only provided me with invaluable industry-grade skills but also served as the direct foundation for my BirdRover research, which went on to win the IRIS Grand Award and represent India at Regeneron ISEF.
- AI algorithms for real-time bird detection and classification
- Embedded system integration with Siemens professional-grade platforms
- Custom PCB design for drone subsystems
- Implementation of UART, SPI, and I2C communication protocols
- Convolutional Neural Networks (CNNs) with TensorFlow for onboard inference
- Autonomous drone navigation tailored for environmental monitoring
- Adapting CNN models to run efficiently on embedded hardware
- Ensuring reliable data transfer over UART-based SD card storage
- Integrating AI with legacy embedded workflows
- Testing and validating AI performance in real-world environmental conditions
- Served as the foundation for my BirdRover project, later awarded the IRIS Grand Award
- Contributed to Siemens engineers’ AI integration efforts in embedded systems
- Demonstrated the real-world application of AI-powered drones in conservation
- Bridged the gap between embedded systems and artificial intelligence in UAV design