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    In conclusion, this thesis concentrated on analyzing wire and mesh images at the pixel level for the detection of the landslide containment components. The research collected a data-set of landslide containment device images from various locations in Sicily and conducted various pre-processing techniques on it. The Unet and Unet++ models were trained on the data-set, and binary  segmentation tasks were performed in order to do a model selection. Additionally multi-class semantic segmentation approach is also employed and obtained results are compared to do a model selection between multi-class and binary semantic segmentation model. The best model was determined by testing different optimization algorithms and loss function combinations. Further experiments were carried out by varying the filter sizes and using smaller encoder-decoder blocks to assess the impact on performance. 

    U-Net and U-Net ++ architectures are exploited and experiments are done on wire and mesh images separately. Even-though U-net++ model gave slightly better results, the standard U-Net model is selected due to the fact that it is a lighter weight and less complex network compared to U-Net++, and there is a slight improvement that is negligible considering the complexity of the networks.
    Multi-class semantic segmentation gave poorer results as it is expected because conducting a binary segmentation is less complex task for models to conduct. Considering the compromise between employing a single model to detect both objects at pixel-level at the same time and accurately obtaining the segmentation results, model selection is decided on Binary U-Net model.
    After model selection, the focus turned on the grid search to with different combination of hyper-parameters. During grid search different loss functions, optimizers, different number of encoder decoder blocks and different filter sizes are employed.
    The results showed that the best model for wire is U-Net with dropout layers, Adam optimizer and binary cross entropy loss.
    The best model for mesh is achieved by U-Net with smaller encoder decoder blocks, Adam optimizer and binary cross entropy loss with learning rate 0.0001 on data augmentation applied data-set.
    
    Possible future development is using the best model with best parameters to identify the objects in landslide containment devices and applying an anomaly detection technique to detect  abnormal regions accordingly. This process generally focuses on analyzing the shape, texture, color and other features of the region of interest of the objects, and making a comparison with the normal state of the objects to see the deviation from the normal state. This can be done by determining a predefined thresholds to do inference whether an anomaly is crucial for further investigation and to decide if it needs a repair.  Thus, achieving robust and accurate object anomaly detection is possible by combining semantic segmentation with anomaly detection techniques. Thereby, harmful damages by landslide hazards or rockfall situations can be prevented.
    
   