How to choose the right camera for deep learning software?
Figure out how to find the perfect vision hardware for deep learning solution
If your company develops deep learning software your data could be generated through object detection. In order to get high accuracy results choosing the right camera for your software is vital, since the camera you choose has a big impact on the quality and consistency of the data. So, what do you need to look for when searching for the best camera for your software?

GPU optimized for imaging 

When your software is depending on the camera to deliver high-quality images, make sure to look into the processor that is being used to process the images. You’ll want to choose a GPU optimized for imaging algorithms. But that’s not all. It’s also important to look at the correct cooling solution for your GPU and sometimes even the amount of sound it produces can be relevant for your tasks.

Location, location, location

Using cameras to generate data can be a lot harder than it sounds. There are multiple factors to take into consideration and where you place the camera might be one of the most undervalued factors. How high, how low, which direction to face? Is there enough light or does sunlight blind the camera? They all have a big impact on your data. There are two things that can make the choice of location a little easier. First: how easy is the camera to install? Can it be installed on every surface or in small corners? How big is it and how many cables do you need?  

Another thing that helps choose a location, is the temperature at which the camera will remain working. Some cameras have a small temperature frame and others can keep it cool (or warm) in more extreme situations. 


I want it all and I want it now!

Choosing the right camera for your goals can also depend on how long the data you’re collecting is relevant or applicable. When you work with time-sensitive data you will need a camera that provides real-life results generated with edge computing. Are timely reactions (latency) not a crucial factor? Then a camera that works with cloud computing is suitable.

Communication is key

Open-source cameras make it easier for developers to implement the cameras in their solutions. ONVIF takes open source a step further. The surveillance industry created ONVIF as an open standard protocol that allows cameras to communicate with each other and with network recording devices. So, when you’re choosing a camera, also consider using a camera that is ONVIF compliant. This will ensure the camera can be used in your current project, but also in future projects where you might be using other cameras or a different video management software.

Choosing the right sensor

A higher megapixel count does not always result in better images. It’s the focus and the sensor that make the difference between a blurry or high-detail image. The sensor of a camera captures the light reflected from objects and converts it into an image. When choosing a sensor for precision measurements, low light situations, or fast-moving objects, you’ll want to go for the bigger sensor, regardless of the pixel count.

Edge computing vs. cloud computing - All you need to know
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