How to leverage our AI (Computer Vision) to do deep analysis from a picture of PCBA (Printed circuit board assembly)?

Question

We have done lots of Teardown from our devices competitors, and there are lots of pictures of PCBA from those teardowns , and we would like to understand such as total component counts, numbers of component per each components type on those PCBA via pictures? How can we find the right team or people to help internally if need more details?

Answer 1

Hi James Li,
To leverage AI, specifically Computer Vision, for deep analysis of PCBA (Printed Circuit Board Assembly) images, you can follow these steps:

• Collect teardown images, ensuring high resolution and proper labeling of component types and counts. Enhance image quality using preprocessing techniques such as noise reduction and contrast adjustment.
• Choose a pre-trained computer vision model (e.g., Azure Custom Vision, YOLO, or Mask R-CNN) or train a custom model using your labeled dataset. Utilize Azure Cognitive Services or OpenAI's Vision API for object detection and classification.
• Use the trained model to identify and categorize components on the PCBA, extracting insights such as total component count and distribution. Apply OCR (Optical Character Recognition) to read part numbers and labels.
• Run inference on new images, leveraging AI to compare different PCB designs, detect anomalies, and gain insights into competitors' component choices.
• Deploy the trained model into your workflow using Azure ML, APIs, or SDKs. Automate the analysis pipeline to process new teardown images efficiently and generate reports.

Hope this helps. Do let us know if you have any further queries.

---

If this answers your query, do click Accept Answer and Yes for was this answer helpful.