Recently I started working on convolutional neural networks (cnn) for obstacle avoidance. With the help of "DroNet" from ETH Zurich and the "Deep Learning for Computer Vision" book from Adrian Rosebrock, I managed to build my first cnn algorithm for obstacle avoidance. The cnn module is written in python, using a keras module with tensorflow backend. I included my cnn algorithm in a dronekit script. By sending mavlink distance messages to the flight controller in loiter or altitude hold flight mode, my drone is able to avoid obstacles. A brief description and demonstration of the developed cnn is given in the YouTube video. First results are promising.