Automated indoor Surveillance Quadcopter with image recognition using OpenCV via Canny edge detection for avoiding stationary obstacles

The thesis involves the design of an automated indoor surveillance Quadcopter with image recognition using OpenCV via canny edge detection to avoid stationary obstacles an unmanned aerial vehicle surveillance drone which will be used for monitoring of a specified indoor area surveillance. The quadcopter will be designed using the DJI F450 airframe flamewheel frame mounted with an IP camera. Securing the area will be done through the IP camera (surveillance) which can also be a sensor to avoid stationary obstacles. The image recognition system will be used and applied as an algorithm for detecting the edges of the indoor area called canny edge detection. The quadcopter will be automated due to the microcontroller mini Gizduino w/ Atmega 328 which utilizes Arduino Integrated Development Environment and C/C++ language that will be used in programming the microcontroller for collision detection, avoidance, and automated navigation. Hobby King/Holybro MultiWii 328P Flight Controller is used for the flight stability.