Fence surveillance with convolutional neural networks

Sammanfattning: Broken fences is a big security risk for any facility or area with strict security standards. In this report we suggest a machine learning approach to automate the surveillance for chain-linked fences. The main challenge is to classify broken and non-broken fences with the help of a convolution neural network. Gathering data for this task is done by hand and the dataset is about 127 videos at 26 minutes length total on 23 different locations. The model and dataset are tested on three performances traits, scaling, augmentation improvement and false rate. In these tests we concluded that nearest neighbor increased accuracy. Classifying with fences that has been included in the training data a false rate that was low, about 1%. Classifying with fences that are unknown to the model produced a false rate of about 90%. With these results we concludes that this method and dataset is useful under the right circumstances but not in an unknown environment.