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This article brings a proposal to implement a passageway detector for a drone, like windows, doors, holes etc. In this case, it was applied on the model Tello of Ryze Tech. The modeling technique uses a Neural Convolutional Deep Learning Network, with a pre-supervised training. This training is done with three image classes: unobstructed path, obstructed path, and the passageway inside of the path, to a specific environment, such as a school or a hospital. After the detection of a way defined by the user, the technique uses image filters to find a polygon through the window and compare the returned data with the values stored in the neural network dataset. To find the best parameters to identify the passage in the processing, the algorithm makes an adjustment through parameters interpolation that allows the drone to perceive its crossing for many cases of environmental variations. The Network model used is the SSD implemented on Google’s TensorFlow framework and for the image processing, it uses filters and functions from OpenCV library, where both codes are implemented in Python programming language. |
