For my final project, I would like to create a system that uses neural object detection, from a camera feed, in order to recognise certain objects and avoid them. The system would mimic the behaviour of certain herbivore animals, such as rabbits, impala or kudu, which can often spot known predators and initiate a flight reaction. It is one of the three (fight, flight, freeze) known survival strategies during conflicts between animals of different species. Herbivores are not biologically equipped to fight predators due to their chewing teeth, as well as relatively small bodies. However, the necessity of running away from various predators means these animals often develop fast running legs, and an instant recognition of certain triggers such as the shapes and patterns of predators.
For my system, I would like to use a Raspberry-Pi based computer, equipped with a webcam, which would record video in real time. The camera feed will be forwarded to a python sketch that recognises certain objects, such as predatory animals (tigers, lions, etc. ) and propels servo motors to move in the opposite direction as fast as possible. The idea is largely prompted by the Medium article I found on that specific technology: https://heartbeat.fritz.ai/real-time-object-detection-on-raspberry-pi-using-opencv-dnn-98827255fa60
In my project, I would like to use this tutorial and experiment with the results of it. Perhaps it would be possible to adjust the system to recognise other objects, and make a potential use case for some industry other than experimental robotics.