AI‑Powered Real-Time Structural Health Monitoring Using Crack Detection, Vegetation Segmentation, and Depth Analysis

Rijja H, Rohith Varshighan S, S. Veeramachaneni, Sumedha Maharana

TL;DR: An AI-driven system that can provide comprehensive structural health diagnostics from a single input structural image, using six parallel computer-vision pipelines with experimentally validated real-time inference performance, forms a strong base toward automated, scalable, and data-driven structural health monitoring.

Context / Content: Civil infrastructure and heritage structures deteriorate with time due to environmental exposure, material aging, moisture ingress, pollution, and biological growth. Traditional inspection relies heavily on manual assessment, which is slow, risky, and subjective, especially in high‑rise or fragile heritage settings. The existing digital tools have concentrated mostly on 2D crack detection and have not provided depth estimation, biological segmentation, or integrated multi‑view analysis. In order to overcome these limitations, the present work proposes an AI-driven system that can provide comprehensive structural health diagnostics from a single input structural image, using six parallel computer-vision pipelines with experimentally validated real-time inference performance. Objectives: - Automate crack detection with lightweight deep‑learning models Identify and segment the biological growth that accelerates surface decay. - Estimate depth variations to show severity and possible spalling. - Provide integrated analysis across image processing and 3D heightmap generation - Reduce unsafe manual inspections and support heritage preservation - Scalable Structural Monitoring for Smart‑city and Cultural‑heritage Applications Methods: The system integrates several models and algorithms: - Crack Detection → lightweight R-CNN optimized for 25 epochs - Biological Growth Segmentation → U-Net–based pixel mask generation - Depth Estimation → MiDaS monocular depth for surface profiling - Material Analysis (Optional) → lightweight MobileNetV2 classifier Edge Detection → Canny‑based structural contour extraction - Integrated Analysis → Real-time processing across image analysis and 3D heightmap tabs The pipeline runs on standard laptop CPUs without specialized hardware, sustaining ~14–23 FPS depending on the task, validating its suitability for real-time field inspection. It has three primary analysis tabs: Image Analysis for crack and vegetation detection, 3D Heightmap for depth visualization and surface profiling, and supporting analytics. Results / Conclusions: Tests run on 11,654 images, the system achieves real-time inference speeds of 0.0683 seconds per image for crack detection and 0.0424 seconds per image for segmentation on standard CPU hardware, enabling practical deployment without GPU dependency for concrete, brick, stone, and heritage materials demonstrate robust crack detection and strong segmentation performance for biological growth. Depth maps provide enhanced structural insight beyond traditional 2D inspection methods. Principle integrated analysis allows for handling: - Original Image - Crack Detection - Vegetation Segmentation - Material/Surface Mask - Depth Map - Edge Detection This significantly reduces the time taken for inspection and assists the engineers and conservation teams in the identification of defects even at their early stages. Although field deployment and full 3D reconstruction are beyond the scope of this phase, it forms a strong base toward automated, scalable, and data-driven structural health monitoring.