Here is how WarpDriven Factory AI approaches it, based on what we publish.

What it detects: more than 23 types of common garment defects in four groups: sewing and structure (broken threads, skipped stitches and similar), fabric (slubs, snagged yarn), appearance and colour (stains, shade differences), and workmanship and shape (twisted legs, creases). The exact list is set by your products and your inspection standards.

How a piece is checked, taking a typical jeans (trousers) station as the example: the operator hangs the pair on a pneumatic fixture, it is inflated into shape under fixed lighting, and 8–12 cameras scan the front, then the back after an exact 180° turn, then the inside after it is turned over. The fixture and station setup are designed around the garment type, so other products are assessed case by case. Each piece is judged pass or fail against the defect standards and thresholds you confirm, with the images and a record kept for every garment. Inference runs on an industrial PC in your factory, and raw images stay on site.

How accurate it can be depends on the fabric, the colour and the type of defect, so there is no single honest number. We first assess your samples, then learn a baseline from your real line at a pilot station, and the acceptance criteria are written into the contract.

What to send first: a batch of photos of defective and good samples, plus your inspection standards. We run a free assessment on them.

Timing and cost: from requirements review to acceptance and training it usually takes two to three months, depending on the number of stations, hardware delivery and baseline learning. You can start with one or two stations as a pilot. We quote by the number of stations, hardware configuration and scope of service, with hardware, software and services listed separately.

Learn more: https://warpdriven.ai/factory-ai

Book a free line assessment call: https://calendly.com/cubean/30min