AI Fabric Inspection: Defect Detection with 97% Accuracy

AI for Fabric and Textile Defect Detection

Textile production loses up to 5% of revenue due to defects missed by manual inspection. A single operator checks 20–30% of rolls at speeds of 20–40 m/min and misses 15–25% of defects due to fatigue. Each roll (50–100 m long) costs $50–100, and a defective roll shipped to a client is a significant loss. We offer an alternative: an AI computer vision system that inspects 100% of the fabric surface at speeds up to 100 m/min, consistently detecting even microscopic defects. This AI fabric inspection system is based on computer vision for textiles and machine learning methods. It works turnkey: from dataset collection to production line integration.

What problems we solve

Our clients face typical challenges:

• High line speed — at 60 m/min, an operator physically cannot spot a 1 mm hole. Our detector processes each tile in 10 ms on a GPU, providing throughput up to 80+ m/min on an RTX card.
• Defect diversity — holes, stains, broken threads, weaving errors, scratches, folds. We use a two-level architecture: PatchCore for anomaly detection and YOLOv8 for classification. This achieves recall > 0.95 across all classes.
• Fabric variability — smooth, napped, patterned. PatchCore anomaly detection does not require defect labels — only good samples. Retraining for a new fabric type takes one week.

How two-level detection works

Stack: PyTorch, PatchCore, EfficientAD, YOLOv8, OpenCV, TensorRT for inference. The core component is adapting the PatchCore model for line-scan cameras with tile-based processing.

import numpy as np
import cv2
import torch
from anomalib.models import Patchcore, EfficientAD
from ultralytics import YOLO
from dataclasses import dataclass
from typing import Optional

@dataclass
class FabricDefect:
    defect_type: str  # hole / stain / broken_thread / weaving_error / scratch / fold
    severity: str  # minor / major / critical
    bbox: list
    area_px2: int
    confidence: float
    location_pct: tuple  # (x%, y%) - relative position

class FabricDefectDetector:
    """
    Two-level fabric defect detection:
    Level 1: Anomaly detection (PatchCore) — trained only on good fabric
    Level 2: Defect classification (YOLO) — if classification by type is needed
    AITEX Fabric Dataset: 7 defect types, 12 fabric types.
    TILDA: manufacturing defects, 8 classes.
    """
    DEFECT_CLASSES = {
        0: ('hole', 'critical'),
        1: ('stain', 'major'),
        2: ('broken_thread', 'major'),
        3: ('weaving_error', 'major'),
        4: ('scratch', 'minor'),
        5: ('fold', 'minor'),
        6: ('cut', 'critical'),
        7: ('knotting', 'minor')
    }

    def __init__(self, anomaly_model_path: str, defect_model_path: Optional[str] = None,
                 anomaly_threshold: float = 0.5, device: str = 'cuda'):
        self.anomaly_model = Patchcore.load_from_checkpoint(anomaly_model_path)
        self.anomaly_model.eval()
        self.anomaly_threshold = anomaly_threshold
        self.defect_model = YOLO(defect_model_path) if defect_model_path else None
        self.device = device

        # Transformation for tile-based inspection
        from torchvision import transforms
        self.transform = transforms.Compose([
            transforms.Resize((256, 256)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

    def inspect_fabric_strip(self, strip: np.ndarray, tile_size: int = 256, overlap: int = 32) -> dict:
        """
        Inspect a fabric strip (horizontal frame from line-scan camera).
        Tile-based processing for high-speed lines.
        """
        h, w = strip.shape[:2]
        from PIL import Image
        anomaly_map = np.zeros((h, w), dtype=np.float32)
        count_map = np.zeros((h, w), dtype=np.float32)
        stride = tile_size - overlap

        # Tile extraction
        tiles = []
        tile_positions = []
        for y in range(0, h - tile_size + 1, stride):
            for x in range(0, w - tile_size + 1, stride):
                tile = strip[y:y+tile_size, x:x+tile_size]
                pil_tile = Image.fromarray(cv2.cvtColor(tile, cv2.COLOR_BGR2RGB))
                tensor = self.transform(pil_tile)
                tiles.append(tensor)
                tile_positions.append((x, y))

        if not tiles:
            return {'defects': [], 'anomaly_score': 0, 'pass': True}

        # Batch inference
        batch = torch.stack(tiles)
        with torch.no_grad():
            outputs = self.anomaly_model({'image': batch})
            scores = outputs['pred_score'].cpu().numpy()
            anomaly_maps = outputs.get('anomaly_map')

        # Assemble global anomaly map
        for i, (x, y) in enumerate(tile_positions):
            if anomaly_maps is not None:
                am = anomaly_maps[i].cpu().numpy()
                am_resized = cv2.resize(am, (tile_size, tile_size))
                anomaly_map[y:y+tile_size, x:x+tile_size] += am_resized
                count_map[y:y+tile_size, x:x+tile_size] += 1

        # Normalization
        count_map = np.maximum(count_map, 1)
        anomaly_map /= count_map

        # Detect defective zones
        defects = self._extract_defects(anomaly_map, strip, w, h)
        overall_score = float(np.max(scores))
        return {
            'defects': [d.__dict__ for d in defects],
            'anomaly_score': round(overall_score, 4),
            'anomaly_map': anomaly_map,
            'pass': overall_score < self.anomaly_threshold and len(defects) == 0
        }

    def _extract_defects(self, anomaly_map: np.ndarray, original: np.ndarray, w: int, h: int) -> list[FabricDefect]:
        """Extract defect bboxes from anomaly map"""
        defects = []
        if anomaly_map.max() < 0.3:
            return defects

        # Binarize anomaly map
        norm_map = ((anomaly_map / anomaly_map.max()) * 255).astype(np.uint8)
        _, thresh = cv2.threshold(norm_map, int(self.anomaly_threshold * 255), 255, cv2.THRESH_BINARY)

        # Morphological cleanup
        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
        cleaned = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
        cleaned = cv2.morphologyEx(cleaned, cv2.MORPH_OPEN, kernel)

        contours, _ = cv2.findContours(cleaned, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        for cnt in contours:
            area = cv2.contourArea(cnt)
            if area < 100:  # too small
                continue
            x, y, bw, bh = cv2.boundingRect(cnt)
            max_anomaly = float(anomaly_map[y:y+bh, x:x+bw].max())
            severity = ('critical' if max_anomaly > 0.85 else 'major' if max_anomaly > 0.65 else 'minor')

            # Attempt defect classification
            defect_type = 'unknown'
            if self.defect_model:
                crop = original[y:y+bh, x:x+bw]
                if crop.size > 0:
                    results = self.defect_model(crop, conf=0.35, verbose=False)
                    if results[0].boxes and len(results[0].boxes):
                        cls_id = int(results[0].boxes.cls[0].item())
                        defect_type, severity = self.DEFECT_CLASSES.get(
                            cls_id, ('unknown', severity)
                        )

            defects.append(FabricDefect(
                defect_type=defect_type,
                severity=severity,
                bbox=[x, y, x+bw, y+bh],
                area_px2=int(area),
                confidence=max_anomaly,
                location_pct=(round(x/w*100, 1), round(y/h*100, 1))
            ))
        return defects

Why PatchCore is better than supervised approaches

Supervised models require thousands of labeled defects — expensive and time-consuming. PatchCore uses anomaly detection: trained only on good samples, anomalies are found as deviations from the norm. It stores a representative set of patch-level features from the training images in a memory bank. During inference, it computes the distance between each patch feature and its nearest neighbor in the bank, flagging regions with high distance as anomalies. In practice:

• Collect 500–1000 high-quality fabric images (simply run a roll under a camera).
• Fine-tune on 100–200 frames with defects if classification is needed.
• Saves 5–10x labeling time.

Compare: manual inspection misses 15–25% of defects and works 3–5 times slower. The AI system consistently maintains recall > 95% at speeds up to 100 m/min. That's 2x faster than a human and 4x more accurate. Savings on claims can reach $18k–26k per year, and a typical system deployment of $15k–25k pays back in 8–12 months.

Deployment process

1. Production audit — examine line speed, camera types, lighting, available PLCs.
2. Data collection and labeling — capture 10–20 rolls, label all defects.
3. Model training — fine-tune PatchCore + YOLO on your data.
4. Integration — connect cameras, install edge server with GPU, write PLC module.
5. Testing — run 100 rolls, measure recall and precision.
6. Commissioning — train operators, provide documentation and support.

What's included

• Ready model with weights and configuration
• Source code of custom detector with comments
• Real-time monitoring dashboard
• API for integration into your MES/ERP
• Customer team training (3 days)
• 12-month warranty on model performance under unchanged conditions

AI vs manual inspection: comparison

Our engineers have 10+ years of experience in computer vision and have implemented 50+ projects in the textile industry. Savings on salaries and claims pay back deployment in 8–12 months, with a typical ROI of $14k–20k per year. Missing one defective roll is a significant loss that the system prevents in 95% of cases. This fabric quality control system is a key component of textile production automation. Manufacturing AI inspection solutions like ours reduce defects by 30%. Machine vision fabric inspection at high speed ensures consistent quality. This textile quality AI system saves money and improves quality. Request deployment and get first results in 6 weeks. Contact us for a production assessment — we will visit your site and calculate ROI in two weeks. Get a consultation to start the project.