AI inventory of construction materials from photos: turnkey development

The problem of manual counting on construction sites

Manual inventory of materials on a construction site is a source of errors and losses. Rebar, bricks, cement bags, pipes, plywood sheets — all of this can be counted automatically from a phone or drone camera photo. We develop turnkey computer vision systems for AI counting of construction materials: from dataset collection to integration with your WMS or 1C. Clients save up to 80% of time on inventory and eliminate the human factor. Average budget savings reach 1.5 million rubles per year per warehouse.

How does an AI system handle dense stacks?

Classical detectors (e.g., YOLOv8) fail when objects lie close together: IoU > 0.6, NMS suppresses correct detections. Accuracy drops to 60–70%. For such cases we use density estimation — an approach borrowed from crowd counting. The network predicts a density map whose pixel sum ≈ number of objects. For bags in stacks, accuracy rises to 91–95%. Density estimation outperforms YOLO detection by 1.3–1.5 times in accuracy on dense stacks. Density estimation is a method adapted for industrial accounting.

class DensityBasedCounter:
    """
    CSRNet or CrowdCounting approach adapted for materials.
    Instead of detecting each object, we predict a density map.
    The sum of the density map pixels ≈ number of objects.
    """
    def __init__(self, model_path: str):
        import torchvision.models as models
        # VGG16 backbone + density head
        self.model = self._build_csrnet()
        self.model.load_state_dict(torch.load(model_path))
        self.model.eval()

    @torch.no_grad()
    def count(self, image: np.ndarray) -> dict:
        from torchvision import transforms
        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406],
                                  [0.229, 0.224, 0.225])
        ])

        tensor = transform(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
        tensor = tensor.unsqueeze(0)

        density_map = self.model(tensor)[0, 0].numpy()
        # The sum of the density map = number of objects
        count = float(density_map.sum())

        return {
            'method': 'density_estimation',
            'count': round(count),
            'count_float': count,
            'density_map': density_map
        }

Why is density estimation more accurate than a detector for bags?

For bulk materials and objects in dense rows, a classical detector fails. YOLO detection loses to density estimation: accuracy drops to 60–70% vs. 91–95% for the density method. The density map approach yields more stable results on overlapping objects. It is based on the CSRNet architecture, originally developed for people counting in images, but also works well for construction materials.

How do we collect and label the dataset?

Training requires 1000–3000 labeled photos of materials in different angles and conditions. We help organize the shooting or accept ready images. The more diverse the dataset, the higher the accuracy. Labeling is done manually by qualified operators using polygon annotation. We create a separate training set for each material — bricks, rebar, bags, pipes. This allows the model to accurately count objects even under partial occlusion.

Case study: rebar warehouse inventory (our client)

A major construction materials distributor with a warehouse of 800 tons of rebar in bundles, 6 standard sizes (d8 to d32). Manual inventory: 2 people, 8 hours per month. After implementation: an operator photographs the ends of bundles with a tablet (20–30 photos per hour). The model counts the number of rods in each bundle: Hough circles for d12–d25, density estimation for d8.

• Count accuracy: ±2% of manual recount
• Inventory time: 1.5 hours vs. 8 hours manually
• Integration with 1C: automatic stock updates
• Annual savings: over 1.5 million rubles

How to set up AI counting for your materials?

The implementation process includes several stages. First, we audit your warehouse and material types. Then we organize photo shooting from different points and label the dataset. After training the model and testing on a control sample, we deploy the solution on your server or edge device. Integration with the accounting system is the final step. At each stage we provide an accuracy report and recommendations for fine-tuning.

What is included in the development of an AI counting system

• Process audit: analysis of current accounting methods, material types, and shooting conditions
• Dataset collection and labeling: 3000+ images with polygon annotation
• Model training: selection of architecture (YOLOv8, CSRNet, SAM) and hyperparameters
• API development: REST or gRPC for integration with your WMS/1C
• Testing: A/B test on real data, accuracy report
• Deployment: server (Docker, Kubernetes) or edge (Jetson, Raspberry Pi)
• Documentation and training: operator manual, 1-hour session
• Support: 3 months of warranty maintenance

Common difficulties during implementation

• Lighting variety: from bright sun to warehouse twilight — dataset augmentation is required.
• Occlusion and dirt: dirt on objects reduces accuracy — we use preprocessing and filtering.
• Different batches: brick shades may vary — the model must generalize across color variations.

Timeline and cost

Development cost is calculated individually and depends on the number of material types, data quality, and integration complexity. The investment typically pays back in 6–12 months due to reduced labor hours and fewer errors. We guarantee accuracy of at least 90% on the pilot project. Contact us for a consultation — our engineer will assess your task and propose the optimal solution. Order a free preliminary evaluation to discuss your project details.