Rapid Damage Detection Using Texture Analysis with Neural Network

Abstract

Damage detection in many contexts, such as buildings, infrastructure, and natural landscapes, must be done quickly and accurately for successful reaction and recovery activities. Because of its capacity to capture subtle patterns and features, texture analysis has shown to be a beneficial tool in identifying and characterizing damage. This research describes research that combined texture analysis with neural networks to identify damage quickly. To automatically train discriminative features from image textures, the proposed technique takes advantage of deep neural networks’ capabilities. Using an enhanced and specifically trained convolutional neural network (CNN), the underlying texture patterns of the damaged regions are recorded. The network learns to recognize distinct textural properties suggestive of damage via lengthy training on labelled datasets comprising both damaged and undamaged samples. A diversified collection of photos representing various sorts of damage events is used to test the performance of the suggested technique. The collection contains examples of building damage, environmental calamities, and other observable harm. The usefulness of the texture-based neural network method in identifying and localizing damage is assessed using comparative analyses, and its performance is compared to standard image processing approaches. Early results demonstrate that the proposed method is more effective than existing approaches for rapidly detecting damage. The trained neural network detects and analyses small textural differences in pictures, allowing for accurate detection and localization of damaged areas. Furthermore, the network is capable of identifying damage in a variety of environmental situations and damage kinds.