Simplifying AI for Engineers: 5 Models for Building Defect Detection, Explained with Simple Analogies

We often hear about how AI is changing the world, but how exactly does it help solve practical tasks like building inspections? When a drone transmits thousands of photos of a facade, they need to be analyzed. This technology is based on specific neural network architectures and approaches. Let’s explore 5 key ones using simple analogies.

1. Approach: Image Classification (VGG, ResNet Architectures) — The Sorting Center

Analogy: Imagine you’re working at a mail sorting center. Your task is to quickly look through thousands of packages and sort them into two bins: “Standard Delivery” and “Requires Special Attention” (e.g., fragile or damaged items). You don’t look inside; you make an instant decision based on general features.

How it works in IT: At the core of this approach are Convolutional Neural Networks (CNNs) with classification architectures like VGG or ResNet. Such a model analyzes an entire image and delivers a verdict — which class it belongs to. In our case, there are two classes: “defect present” or “no defect.” These types of architectures are “champions” in pattern recognition and can capture patterns that indicate a problem.

Application to building defects: This is the first and fastest stage of analysis. The model scans the entire photo archive from the drone and filters out 95% of the images of a perfect facade. As a result, the engineer receives only those shots where the AI suspects the presence of cracks or spalls for detailed study, saving dozens of hours of routine work.

Best suited for: Quick initial filtering of vast amounts of visual data.

2. Model: YOLO (You Only Look Once) — The Experienced Customs Officer

Analogy: Picture a customs officer standing by a luggage conveyor belt. He doesn’t stop the belt. Instead, he casts one quick, trained glance and instantly determines: “Something suspicious in that blue suitcase, but that green backpack looks fine.” He does it all in a single pass.

How it works in IT: YOLO is a famous architecture for the task of object detection. Its key feature is processing an image in a single pass. The model divides the image into a grid and, for each cell, simultaneously predicts the presence of an object, its class, and a bounding box. This makes it incredibly fast.

Application to building defects: Models based on the YOLO architecture are ideal for real-time inspections. A drone flies along a facade, and the model marks detected defects with rectangles in a live feed, immediately classifying them: “crack,” “spall,” “corrosion spot.” This allows the operator to assess the overall picture instantly.

Best suited for: Fast, real-time detection and classification of defects.

3. Model: Faster R-CNN — The Crime Scene Investigator

Analogy: Imagine a crime scene investigator arriving at the scene. First, he outlines several zones with chalk where, based on his experience, evidence might be found (Step 1: searching for potential areas). Then, he takes out a magnifying glass and carefully, without rushing, examines each of these outlined zones (Step 2: detailed analysis).

How it works in IT: Faster R-CNN is a classic two-stage architecture for object detection. First, a special module (Region Proposal Network) scans the image and suggests “candidate regions.” Then, a second module analyzes each of these regions in detail, accurately determining the object’s boundaries and its class. This approach is slower but often more accurate than single-stage methods.

Application to building defects: Models with this architecture are used when maximum accuracy, not speed, is crucial. For example, when preparing an official report on the condition of a building, where even the smallest defects need to be precisely identified and counted.

Best suited for: Detailed and high-precision analysis of pre-shot images, where accuracy is more important than speed.

4. Model: U-Net — The Cartographer

Analogy: Imagine a cartographer who needs to map the precise course of a river. It’s not enough to just draw a rectangle and label it “river.” He needs to trace every bend, every tributary, every delta with maximum precision.

How it works in IT: U-Net is an elegant neural network architecture created for the task of semantic segmentation. Its goal is not to find an object in a box, but to classify every pixel of the image. As a result, it creates a precise “mask” that perfectly matches the contours of the object. Its symmetrical U-shaped structure brilliantly handles the preservation and restoration of precise spatial data.

Application to building defects: Models based on U-Net are indispensable for analyzing cracks. They don’t just find a crack; they provide its precise map — with its full length, thickness, and shape. This data allows engineers to accurately calculate the defect’s severity.

Best suited for: Precisely outlining and measuring linear and complex-shaped defects, such as cracks or areas of plaster delamination.

5. Approach: Anomaly Detection with GANs — The Counterfeiter and the Detective

Analogy: Imagine a duo consisting of a brilliant counterfeiter (the Generator network) and an experienced detective (the Discriminator network). The counterfeiter tries to print a perfect copy of a banknote, and the detective tries to spot it. They compete, and over time, the Generator learns to create copies indistinguishable from the original.

How it works in IT: Generative Adversarial Networks (GANs) are a class of models consisting of two competing neural networks. For our task, we can use a trained Generator. First, we train it on thousands of photos of perfect, “healthy” facades. Then, we show it a photo with a defect. The Generator, knowing only what a “perfect” facade looks like, will try to “fix” the image. By comparing the original photo with its “restored” version, we can easily see the difference — this is the defect we’re looking for.

Application to building defects: This method, called “anomaly detection,” is useful when we have few examples of specific defects for training or when we are looking for rare, non-standard damage that other models might miss.

Best suited for: Detecting any deviations from the norm, especially rare and unforeseen types of defects.