In the thesis we address the problem of infantry trench detection in terrain images obtained with the Lidar system. Detection is performed using segmentation convolutional neural network technology, which is currently outperforming other methods when it comes to solving problems which require object detection and image segmentation. Successful detection has historical meaning as well as the automatic detection methods have not yet been used to address this problem. In addition the Lidar system is now offering a view of terrain with unprecedented precision. We present an algorithm based on the U-net architecture together with image preprocessing and post processing steps, as due to the nature of the problem the detection does not need to run in real time. We compare the results of our method (Fr13) with two modified approaches (Fr9 and Canny) and a related method - Edge. Comparison is performed using the F1 and MCC measures where our method outperforms the Edge method by 10% to 30%. Based on the different results achieved by methods Fr13 and Fr9 we present and discuss the implications different methods of learning set generation and image augmentation have on the learning process of neural networks, especially if original data is heavily unbalanced.
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