Computer Vision Damage Detection (Cracks, Dents, Scratches)

Recently, an insurance company approached us: they needed to automatically assess body damage from smartphone photos. The main pain point was tiny scratches and dents that operators missed. We developed a model that detects defects as small as 0.5 mm and classifies them by severity. A localization error could cost millions, so we aim for recall >95% with controlled false positives. Over 5 years in the market, we have completed more than 50 computer vision projects, including defect detection for automotive and metallurgy industries. Manual labor savings reach 80%, and claims drop by 30%. Get a preliminary estimate — contact us for a consultation.

Types of Damage and Detection Specifics

Cracks — thin linear structures with a small width-to-length ratio. Standard detectors perform poorly: bounding boxes are large while the defect is small. Segmentation is preferable.

Dents — surface deformation without material rupture. Hard to detect in 2D; raking light and 3D reconstruction help.

Scratches — similar to cracks, linear structures. Depth affects severity.

How We Detect Fine Cracks and Scratches

We use a combination of YOLOv8 for instance segmentation and specialized preprocessing. Raking light at a shallow angle casts shadows from the tiniest irregularities, revealing defects only a few pixels wide. The pipeline includes top-hat transformations and contrast enhancement.

System Architecture

from ultralytics import YOLO
import numpy as np
import cv2

class DamageDetectionSystem:
    def __init__(self, config: dict):
        # Детектор повреждений (YOLOv8 instance segmentation)
        self.detector = YOLO(config['detection_model'])
        # Классификатор тяжести
        self.severity_classifier = load_severity_model(config['severity_model'])
        # Измеритель размеров (требует калибровки)
        self.pixels_per_mm = config.get('pixels_per_mm')

    def analyze(self, image: np.ndarray) -> dict:
        # Детекция и сегментация повреждений
        results = self.detector(image, conf=0.4, iou=0.5)
        damages = []
        for i, (box, mask) in enumerate(zip(
            results[0].boxes,
            results[0].masks.data if results[0].masks else []
        )):
            damage_type = self.detector.model.names[int(box.cls)]
            bbox = box.xyxy[0].tolist()
            area_px = int(mask.sum().item())
            # Вырезаем регион для классификации тяжести
            x1, y1, x2, y2 = map(int, bbox)
            crop = image[y1:y2, x1:x2]
            severity = self.severity_classifier.predict(crop)
            # Реальные размеры если есть калибровка
            size_info = {}
            if self.pixels_per_mm:
                size_info['area_mm2'] = round(area_px / self.pixels_per_mm**2, 2)
                size_info['length_mm'] = self._estimate_length(mask)
            damages.append({
                'id': i,
                'type': damage_type,
                'severity': severity,
                'bbox': bbox,
                'area_pixels': area_px,
                'confidence': float(box.conf),
                **size_info
            })
        return {
            'damages': damages,
            'total_count': len(damages),
            'has_critical': any(d['severity'] == 'critical' for d in damages),
            'summary': self._generate_summary(damages)
        }

Why Raking Light Is Effective

For cracks and scratches, standard frontal lighting is insufficient. Raking light — a source at a shallow angle to the surface — casts shadows from the tiniest irregularities. We use top-hat transformation to extract fine details and histogram equalization to enhance contrast. This increases recall for small defects by 15-20%.

def process_raking_light_image(image_path: str) -> np.ndarray:
    """Normalized image with raking light"""
    # При правильном освещении на стенде — дополнительная обработка:
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    # Топ-hat трансформация для выделения мелких деталей
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (25, 25))
    tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)
    # Усиление контраста
    enhanced = cv2.equalizeHist(tophat)
    return enhanced

How We Measure Defect Sizes

After detection, we need to estimate real dimensions — crack length or dent area. We calibrate the camera with a reference object. The parameter pixels_per_mm is stored in the system config. Then, using the defect mask, we compute physical sizes. For cracks, we skeletonize the mask for more accurate length.

def measure_crack_length(mask: np.ndarray, pixels_per_mm: float) -> float:
    """Measure crack length from mask skeleton"""
    from skimage.morphology import skeletonize
    skeleton = skeletonize(mask > 0)
    length_px = skeleton.sum()
    return round(length_px / pixels_per_mm, 2)

For precise measurements, calibration using a reference is required. We use a chessboard with known spacing, determine the camera matrix, and derive the pixels_per_mm factor.

Datasets and Metrics

Public datasets:

• NEU Surface Defect — 6 classes of steel defects, 1800 images
• DAGM — texture defects, 10 categories
• AITEX Fabric — fabric defects
• Concrete Crack Images — cracks in concrete

# Example training on NEU Surface Defect
from ultralytics import YOLO

model = YOLO('yolov8m-seg.pt')
model.train(
    data='neu_defect.yaml',
    epochs=150,
    imgsz=640,
    batch=16,
    workers=8,
    optimizer='AdamW',
    lr0=5e-4,
    augment=True,
    degrees=180,  # дефекты могут быть в любой ориентации
    fliplr=0.5,
    flipud=0.5,
    mosaic=1.0
)

Metrics on Different Materials

What's Included in the Work

1. Requirements analysis and dataset collection (shoot defects on your equipment or use ready-made datasets)
2. Development of detection and segmentation model, training with augmentation
3. Integration into your IT infrastructure (REST API, Docker container)
4. Camera calibration and lighting setup (raking light rig if needed)
5. Documentation and operator training
6. 3-month warranty support

The cost of developing such a system depends on complexity and data volume. We evaluate each project individually to define the scope. Get a preliminary estimate — contact us.

Implementation Timeline

Checklist: Production Readiness for AI Defect Detection

Before starting the project, check the following:

• Controlled lighting on the stand (raking or diffuse light).
• Stable camera mount with resolution at least 5 MP for defects from 0.5 mm.
• Historical image archive: at least 200–300 examples of each defect type.
• Clear acceptance criteria: maximum allowed false positive and miss rates.
• Readiness for data labeling (mask annotation) or a pre-labeled dataset.
• Defined SLA: acceptable inspection time per object.

We guarantee transparent support at all stages. Request an engineer consultation — discuss the details of your project. Get a solution that really works.

NEU Surface Defect dataset (Song et al., 2019)