What works best where? Discover which technology really gets your job done!

Sensors - Smart Cameras - Industrial Cameras - Embedded Vision?

Sometimes the smallest choice makes the biggest difference:
Is a smart camera enough for my task? Do I need the flexibility of an industrial camera, or does a sensor do the job?

In this article, I'll guide you through the real-world scenarios, explain where each technology shines and help you match your challenge to the right solution.

When someone asks me which technology to choose, my answer is simple
(and never changed through time):

IT DEPENDS

Sensors: Quick and rugged

Perfect for simple yes/no decisions, with low resolution and very basic applications. Fast and easy to setup. Especially when you don't nned high processing power. When all you need is basic triggering, sensors are often the smartest choice.

Smart cameras: Compact power houses

All-in-one units that combine camera, processor, and vision logic. Ideal for barcode reading, basic to mid-range applications like 2D robotic guidance in space-limited applications or standalone operation. However, they offer limited customization, flexibility and scalability, with processing power remaining the most limiting factor.

Industrial cameras: Flexible scalable workhorses

The right choice for scalable and/or complex projects or evolving tasks. With low resolution and a basic PC often available at lower cost than smart cameras. High resolution/speed cameras connected to (a) powerful PC(s) with extensive sensor/optics options can handle nearly ANY application. Perfect for multi-camera setups and high-resolution inspections. Less turnkey, but highly customizable and cost-efficient at scale

Embedded vision: Price/consumption stars for high volume applications

Compact ARM/Xavier-based systems offering industrial camera flexibility in compact format. Integration takes more time and sensor options are limited. Triggering and timing can also be more complicated. The key advantage is full control over system design, allowing optimization for specific price and power requirements. Highly very cost effective and great for edge computing and IoT integration.

Even if these explanantions may be confusing, I can show an easy decision way:

Most important: Get your light right! The best camera won't help if the illumination is wrong!

Sensors / Smart Cameras are usually easy to borrow or test. If you get your task up and running in your lab QUICKLY, they are likely a good fit. If you can’t achieve results within 1-2 days, DON`T CONTINUE. The software on these sensors is limited. If it’s not working smoothly in a controlled lab environment, it will be even more challenging on the shop floor, where conditions and variations are much greater.

If you encounter the above-mentioned difficulties, the right choice is industrial cameras. Consult specialists to determine the appropriate software which can do the job. Industrial cameras can also be a cost-effective option, particularly in multi-camera systems.

If you have an application you want to repeat 50+ times WITHOUT CHANGE, or if low power consumption is a strict requirement, an embedded system may be the right choice. The rule of thumb here is: Build a first prototype with industrial cameras and get the application running reliably. Only once you´re sure that no features will change and you fully understand the required resolution, data rates and processing power, you should move to embedded hardware. As with industrial systems, it is usually wise to consult a specialist to help to select the most suitable embedded platform.

Quick comparison table

Sensors

Smart cameras

Industrial cameras

Embedded vision

+ simple presence/distance detection tasks
- no image processing or visualization

+ space-saving, standalone inspections
- less flexible, low speed, limited resolution/customization

+ Cost-effective or complex, scalable high performance systems
- requires more setup, higher inital effort

+ 50+ applications, compact, low power, embedded machine vision tasks
- very high integration effort, smaller sensor/components choice

What’s your experience with machine vision selection? Did you already face any of the described limitations?

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