I need an OpenCV-based tool that can spot cracks, mould and surface stains on buildings the moment a camera or video feed sees them. The workflow I have in mind is simple: load a live stream (or still images), run the model, and immediately overlay bounding boxes on any defects it finds. Real-time performance is essential because site inspectors want instant feedback while they walk a property. I already have sample footage and photographs you can start with, but I’m open to recommendations on how many more images we should collect or augment to bring the model up to professional reliability. Here’s what I expect you to deliver: • Python source code (ideally using OpenCV, TensorFlow or PyTorch—whatever gets us solid frame-rate speed). • A trained weights file ready to run on common GPUs or CPU-only laptops used on-site. • A short README that explains installation and how to begin detection from webcam, IP camera or folder of images. • A concise test report showing the model’s precision/recall on a held-out validation set. If you can design the architecture so we can bolt on automated reporting later, even better, but the first milestone is strictly about fast, accurate detection. Feel free to tell me what backbone network or additional libraries you prefer; I’m comfortable taking your expert guidance as long as the end result is smooth and dependable in the field.