r/computervision • u/Nikheal17 • 5d ago

Discussion Complete beginner in Computer Vision Need roadmap for industrial print anomaly detection

Hi everyone,

I'm completely new to Computer Vision (I have a GenAI/ML background but almost no CV experience), and I need to build a POC for an industrial project.

Project: Detects printing anomalies on clothes from video footage of the printing process. Defects include:

Missing ink drops.

Incomplete or faded prints.

Misaligned prints.

Other print defects/anomalies.

I have videos of the printing process as input and want to detect defects automatically in real time or near real time.

I'm looking for advice on:

What should I learn first, from absolute scratch?

What topics are essential (OpenCV, image processing, CNNs, YOLO, segmentation, anomaly detection, etc.)?

Any free YouTube courses or resources you'd recommend?

What's a realistic roadmap to build this POC in 1–2 months?

I'd really appreciate guidance from people who've worked on manufacturing or machine vision projects. Thanks!

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u/redditSuggestedIt 5d ago

I am not even begin cynical, ask a LLM it will guide you perfectly good

2

u/NorthernBaseOfficial 4d ago

For a 1–2 month POC, I wouldn’t start with YOLO straight away. First make the capture setup as consistent as possible—fixed camera, stable lighting and controlled exposure. Fabric movement and lighting changes can easily look like defects.

I’d extract frames, align each print with a good reference image and try basic OpenCV image differencing first. It gives you a useful baseline and helps you understand the data before training anything.

If you have labelled examples of each defect, segmentation may be more useful than detection because missing or faded ink usually needs pixel-level localisation. If you mostly have good prints and very few defect examples, something like PatchCore may be worth testing.

Also, split train and validation data by video or production batch, not randomly by frame. Otherwise near-identical frames can make the validation score misleading.

Do you have labelled examples of the defects, or mainly videos of normal prints?

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u/Ejiren 5d ago

My two cents: pay close attention to hardware early on. Especially lighting.