Question 1

Do we need to buy expensive new 4K cameras?

Accepted Answer

Usually, no. Modern computer vision models are designed to be robust. While higher resolution helps for tiny details (like micro-scratches on glass), standard 1080p or even 720p security cameras are often perfectly sufficient for object tracking, people counting, and standard defect detection.

Question 2

What is 'Edge Deployment' and why is it important?

Accepted Answer

Edge deployment means running the AI model directly on a local device (like an NVIDIA Jetson attached to the camera) instead of sending the video feed to the cloud. This is critical for 3 reasons: 1) Zero latency (instant decisions), 2) Privacy (no video leaves your building), and 3) Bandwidth (you don't crash your network uploading 4K video 24/7).

Question 3

How much training data is required to build a custom model?

Accepted Answer

It depends on the complexity of the task. Thanks to 'Transfer Learning', we don't start from scratch. We take a model that already knows how to 'see' and retrain it on your specific objects. This usually requires between 500 and 5,000 annotated images per object category to achieve high accuracy.

Question 4

Can computer vision work in low light or poor weather conditions?

Accepted Answer

Yes, but it requires specific training. If the model needs to operate at night, we train it on nighttime imagery or infrared/thermal camera feeds. We also use data augmentation to simulate rain, fog, and glare during the training phase so the AI isn't confused by bad weather.

Question 5

What is the difference between Object Detection and Segmentation?

Accepted Answer

Object Detection draws a rectangular 'bounding box' around an object (e.g., 'There is a car in this box'). Image Segmentation is much more precise; it colors in the exact pixels of the object, tracing its exact outline. Segmentation is heavier to process but essential for tasks like medical imaging or precise defect measurement.

Question 6

Can you extract text from blurry or curved surfaces?

Accepted Answer

Yes. Traditional OCR (like reading a flat PDF) struggles in the real world. We use deep-learning-based OCR in the wild, which can read text on curved bottles, moving license plates, or slightly blurry shipping labels by combining object detection with text recognition.

Question 7

How do you handle privacy concerns with facial recognition?

Accepted Answer

If facial recognition is not the primary goal (e.g., you just want to count people in a store), we configure the AI at the edge to immediately blur faces or convert people into anonymous stick figures (Pose Estimation) before the data is saved or sent to the dashboard, ensuring full GDPR/CCPA compliance.

Question 8

How long does it take to train and deploy a custom vision model?

Accepted Answer

A standard object detection model (like recognizing safety helmets on a construction site) takes about 4-6 weeks, including data annotation. Complex projects, such as microscopic defect detection with custom edge hardware, can take 3-4 months.

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