The Construction of the Model for Damage Level Assessment of Critical Infrastructure Objects

This paper introduces a novel model for the assessment of the extent of damage to critical infrastructure which can aid in object inspection and creation of a restoration plan for authorities.The model is based on a regional convolutional neural network architecture that can identify and classify different types of damage, including those caused by natural disasters, accidents, or deliberate warfare attacks, such as shelling and bombardment.The transfer-learning technique is utilised to improve the model's accuracy and speed.The developed model classifies the objects on the image as intact (0), of low damage (1), moderate damage (2), high damage level (3), and demolished object (4).It can help authorised persons assess the damage, and its level and create a plan for the restoration of a particular object.In addition, the key areas of research that need to be addressed to improve the accuracy and effectiveness of the R-CNN-based model for damage level assessment have been identified.These areas include the development of better data sources and the integration of other types of frameworks to provide the most accurate assessment of infrastructure objects' impairment.The current model version requires additional training and fine-tuning to achieve its peak classification accuracy.While the initial results are promising, there are several avenues for improvement.Incorporating focal cross-entropy loss or similar techniques (such as contrastive learning) in future model versions is expected to enhance accuracy, reduce loss, and address bias.Additionally, exploring alternative architectures, such as SSD or YOLO could lead to further improvements.Switching to other platforms like PyTorch and Detectron2 may also enhance performance compared to currently utilised TensorFlow.Furthermore, an alternative approach could expand the predictive capabilities of the model beyond damage level assessment.For instance, it could pinpoint precise damage locations.Creating highly specialised models for specific object types might also boost performance while minimising loss.