Progress Monitoring with Computer Vision

Progress Monitoring with Computer Vision

Overview

This skill implements computer vision for construction progress monitoring. Analyze site images automatically to track completion, detect hazards, and compare physical progress against planned work.

Applications:

• Progress percentage estimation
• Safety compliance detection (PPE, barriers)
• As-built vs BIM comparison
• Material and equipment tracking
• Quality defect detection

Quick Start

import cv2
import numpy as np
from PIL import Image
import torch
from torchvision import models, transforms

# Load pre-trained model for construction scene analysis
model = models.resnet50(pretrained=True)
model.eval()

# Preprocess image
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225])
])

# Load site photo
img = Image.open("site_photo.jpg")
input_tensor = transform(img).unsqueeze(0)

# Analyze