Ultra-Basic Thermal Camera Built Using Arduino Uno

Thermal cameras can cost well into the five-figure range if you’re buying high-resolution models with good feature sets. New models can be so advanced that their export and use is heavily controlled by certain countries, including the USA. If you just want to tinker at the low end, though, you don’t have to spend a lot of scratch. You can even build yourself something simple based on an Arduino Uno!

The build uses Panasonic’s cheap “Grid-EYE” infrared array as the thermal sensor, in this case, a model with an 8×8 array of thermopiles. It’s not going to get you any fancy images, especially at long range, but you can use it to get a very blocky kind of Predator-vision of the thermal radiation environment. It’s a simple matter of hooking up the Grid-EYE sensor to the Arduino Uno over I2C, and then spitting out the sensor’s data in a nice visual form on a cheap TFT screen.

It’s a great introduction to the world of thermal imaging. There’s no better way to learn how something works by building a working example yourself. We’ve featured a few similar projects before, too; it’s all thanks to the fact that thermal sensors are getting cheaper and more accessible than ever!

Low Res Arduino Thermal Camera

Do you know how you see those cheap telescopes at the department store? The box has beautiful pictures that probably came from the Hubble. What you will see is somewhat different. You have to carefully look at [upir’s] Arduino thermal camera project because it intersperses pictures of what you expect an 8×8 sensor will produce with images produced by a much better camera.

The actual project — watch the video below — is undoubtedly neat. An inexpensive 8×8 IR sensor and an 8X8 LED panel join to form a crude but usable thermal camera.

Continue reading “Low Res Arduino Thermal Camera”

Phone Thermal Cameras Get Open Source Desktop Tools

Whenever phone-based thermal cameras are brought up here on Hackaday, we inevitably receive some comments about how they’re a bad investment compared to a standalone unit. Sure they might be cheaper, but what happens in a couple years when the app stops working and the manufacturer no longer feels like keeping it updated?

It’s a valid concern, and if we’re honest, we don’t like the idea of relying on some shady proprietary app just to use the camera in the first place. Which is why we’re so excited to see open source software being developed that allows you to use these (relatively) inexpensive cameras on your computer. [Les Wright] recently sent word that he’s been working on a project called PyThermalCamera which specifically targets the TOPDON TC001, which in turn is based on a project called P2Pro-Viewer developed by LeoDJ for the InfiRay P2 Pro.

Readers may recall we posted a review of the P2 Pro last month, and while the compact hardware was very impressive, the official Android software lacked a certain degree of polish. While these projects won’t help you on the mobile front in their current form, it’s good to know there’s at least a viable “Plan B” if you’re unwilling or unable to use the software provided from the manufacturer. Naturally this also opens up a lot of new possibilities for the camera, as being connected to a proper Linux box means you can do all sorts of interesting things with the video feed.

The two video feeds on the left are combined to produce the final thermal image.

Speaking of the video feed, we should say that both of these projects were born out of a reverse engineering effort by members of the EEVblog forums. They figured out early on that the InfiRay (and other similar models) were picked up as a standard USB video device by Linux, and that they provided two video streams: one being a B&W feed from the camera where the relative temperature is used as luminance, and the other containing the raw thermal data cleverly encoded into a green-tinted video. With a little poking they found an FFmpeg one liner that would combine the two streams, which provided the basis for much of the future work.

In the video below, you can see the review [Les] produced for the TOPDON TC001, which includes a demonstration of both the official Windows software and his homebrew alternative running on the Raspberry Pi. Here’s hoping these projects inspire others to join in the effort to produce flexible open source tools that not only unlock the impressive capabilities of these new thermal cameras but save us from having to install yet another smartphone application just to use a device we purchased.

Continue reading “Phone Thermal Cameras Get Open Source Desktop Tools”

Review: InfiRay P2 Pro Thermal Camera

It probably won’t surprise you to learn that Hackaday is constantly hounded by companies that want us to review their latest and greatest gadget. After all, getting us to post about their product is cheaper, easier, and arguably more effective than trying to come up with their own ad campaign. But if you’ve been with us for awhile, you’ll also know that in-house reviews aren’t something we actually do very often.

The reason is simple: we’re only interested in devices or products that offer something useful or unique to this community. As such, the vast majority of these offers get ignored. I’ll give you an example. For whatever reason, multiple companies have been trying desperately to send me electric bikes with five-figure price tags this year. But since there’s no obvious way to turn that into useful content for the readers of Hackaday, I’m still stuck pedaling myself around like it’s the 1900s. I kid of course…I haven’t dared to get on a bike in a decade.

So I don’t mind telling you that, when InfiRay contacted me about reviewing their P2 Pro thermal camera, the email very nearly went into the trash. We’ve seen these kind of phone-based thermal cameras before, and it seemed to be more of the same. But after taking a close look at the specs, accessories, and claims laid out in the marketing material, I thought this one might be worth checking out first-hand.

Continue reading “Review: InfiRay P2 Pro Thermal Camera”

Thermal Camera Reviewed

We keep thinking about buying a better thermal camera, as there are plenty of advantages. While [VoltLog’s] review of the Topdon TC002 was interesting though, it has a connector for an iPhone. Even if you aren’t on Android, there is a rumor that Apple may (or may be forced to) change connectors which will make it more difficult to connect. Of course, there will be adapters, and you can get a USB C version of the same camera.

Technically, the camera is pretty typical of other recent cameras in this price range, and they probably all use the same image sensor. The camera provides 256×192 images.

Continue reading “Thermal Camera Reviewed”

Thermal Camera Plus Machine Learning Reads Passwords Off Keyboard Keys

An age-old vulnerability of physical keypads is visibly worn keys. For example, a number pad with digits clearly worn from repeated use provides an attacker with a clear starting point. The same concept can be applied to keyboards by using a thermal camera with the help of machine learning, but it also turns out that some types of keys and typing styles are harder to read than others.

Researchers at the University of Glasgow show how machine learning can pull details from thermal images like these quickly and effectively.

Touching a key with a fingertip imparts a slight amount of body heat, and that small amount of heat can be spotted by a thermal sensor. We’ve seen this basic approach used since at least 2005, and two things have changed since then: thermal cameras gotten much more common, and researchers discovered that by combining thermal readings with machine learning, it’s possible to eke out slight details too difficult or subtle to spot by human eye and judgement alone.

Here’s a link to the research and findings from the University of Glasgow, which shows how even a 16 symbol password can be attacked with an average accuracy of 55%. Shorter passwords are much easier to decipher, with the system attacking 6 and 8 symbol passwords with an accuracy between 92% and 80%, respectively. In the study, thermal readings were taken up to a full minute after the password was entered, but sooner readings result in higher accuracy.

A few things make things harder for the system. Fast typists spend less time touching keys, and therefore transfer less heat when they do, making things a little more challenging. Interestingly, the material of the keycaps plays a large role. ABS keycaps retain heat far more effectively than PBT (a material we often see in custom keyboard builds like this one.) It also turns out that the tiny amount of heat from LEDs in backlit keyboards runs effective interference when it comes to thermal readings.

Amusingly this kind of highly modern attack would be entirely useless against a scramblepad. Scramblepads are vintage devices that mix up which numbers go with which buttons each time the pad is used. Thermal imaging and machine learning would be able to tell which buttons were pressed and in what order, but that still wouldn’t help! A reminder that when it comes to security, tech does matter but fundamentals can matter more.

Calibrating Thermal Cameras With Hot Patterned Objects

Thermal cameras are great if you want to get an idea of what’s hot and what’s not. If you want to use a thermal camera for certain machine vision tasks, though, you generally need to do a geometric calibration to understand what the camera is seeing and correct for lens distortion. [Henry Zhang] has shared various methods of doing just that.

It’s all about generating a geometrically-regular thermal pattern.

To calibrate a thermal camera, first you need a thermal pattern. This is like typical test image for a camera or screen, but with temperatures instead of colors. [Henry] explains several methods for doing this. One involves using a grid of nichrome wires to create a thermal pattern for calibration purposes. Another uses discs of cold aluminium inserted into a foam board. Even a simple checkerboard can work, with the black spaces heating up more from ambient sunlight than their neighbouring white spots. [Henry] then explains the mathematical techniques used for calibrating based on these patterns.

It’s a useful primer on the topic if you’re working with thermal camera systems. We’ve looked at some other interesting machine vision topics before, too. If you’ve got any great thermal imaging tips of your own, don’t hesitate to drop us a line!