Transcript:
So today we’re diving into the world of machine vision. Very cool. You it is pretty cool. And you’ve sent over some pretty awesome articles from Flexible Vision. I’m already like just blown away by, you know, how much goes into picking the right camera and lens and lighting setup. It’s a lot more than just point and shoot, right? Right. It’s not just your iPhone. Yeah, no. So how about we just jump right in? Let’s do it. OK. So, you know, when you think about machine vision, we’re essentially
giving a machine the power to see, but in a very specific way. Right, it’s not like human vision where we’re trying to perceive the world and all of its beauty. It’s really about capturing the information that’s needed for a very specific task. Like, is this product effective or where is this object located so we can grab it? Okay, so it’s not about taking pretty pictures. No, It’s about capturing the right information. Right. So let’s break down the essential components. Okay. I’ve got cameras, lenses, and lighting.
What should we tackle first? Well, I think the heart of any machine vision system is the camera. specifically the image sensor. That’s what converts the light into electrical signals that a computer can understand. So there are two main types of image sensor, CCD and CMOS. CMOS sensors are becoming increasingly popular because they’re less expensive and they’re faster at processing images. OK.
which makes them really good for high speed applications. So if I’m on like a production line trying to inspect products as they’re whizzing by CMOS is probably my best bet. You got it. OK, cool. So now we’ve got to decide, do we want color or monochrome? Right. I mean, I would think color provides more information. You’d think so, right? But actually, monochrome sensors are often preferred in industrial settings. Really? Yeah, because they’re much more sensitive to light.
which can be really important in factory environments. Okay. And they can operate at much faster speeds. I see. So they can keep up with those, you know, those really fast moving production lines. So it’s not always about capturing every detail and color. It’s about getting the right information as quickly and clearly as possible. Exactly. Gotcha. Okay. So now I’ve also seen these terms, you know, rolling shutter and global shutter. yeah. What’s the difference and why should I care about that? Great question.
So a rolling shutter captures the image line by line, kind of like if you’re scanning a document. OK. And that can actually cause distortion if the object you’re trying to capture is moving quickly. I see. A global shutter, on the other hand, captures the entire image at once. OK. So there’s no distortion. Gotcha. So if I’m dealing with fast moving objects, a global shutter is essential. Yes, definitely. To get an accurate image. Exactly. OK, cool.
So beyond the type of sensor, you also need to consider sensor size, resolution, and frame rate. Yes, absolutely. So can you break those down for me? Sure. So sensor size is basically the physical dimensions of the sensor. And generally, larger sensors can gather more light, which can be a good thing if you’re in a low light situation. OK. Resolution refers to the number of pixels on the sensor. Right. So a higher resolution sensor will be able to capture more detail. OK. And frame rate.
is how many images the camera can capture per second. Okay. So if you need to capture really fast motion, you’re gonna want a camera with a high frame rate. So it’s a lot like Goldilocks and the three bears. Uh-huh. Not too big, not too small. Right. Just right. Exactly. From the application. Okay, cool. So now, even with like the perfect camera, we still need the right lens to focus that light onto the sensor. Right.
And I think that’s where it gets really fun. We had these manual lenses, autofocus lenses, and even liquid lenses. Liquid lenses, yeah, those are pretty cool. Yeah, what’s the advantage of a liquid lens? Well, they can change their shape really quickly. To maintain focus, which is really useful if the object you’re trying to image is moving around a lot, or if the distance to the object is constantly changing. Gotcha, so like on a fast moving production line, or if you have like a robot arm, that’s picking up and placing objects at different depths. Exactly.
A liquid lens could just keep everything in focus. Yeah, it’s basically instantaneous focusing. Wow. Okay, so we’ve got our camera, we’ve got our lens. Now you’ve mentioned that lighting is even more important than the camera itself. You know, it really is. Why is that? Well, think about it this way. If you try to take a picture with your phone in a dimly lit room, it’s going to be all grainy and blurry, and you won’t be able to see any detail. The same principle applies to machine vision.
know, lighting is all about creating contrast and highlighting the features that you want the computer to see. Okay, so it’s not just about brightness. It’s about like strategically using the light to get the best possible image for analysis. Okay, so what are some of the lighting techniques that are commonly used? there are a ton, but some of the most common ones are backlighting, dark field, and diffuse lighting.
Okay. So with backlighting, you’re basically shining the light from behind the object. Okay. Which creates a silhouette that can be really useful for measuring dimensions or detecting the presence or absence of a feature. Dark field lighting uses low angle illumination to highlight surface imperfections or edges. And then diffuse lighting provides kind of even illumination over the entire object. Okay. So it minimizes shadows and glare.
So it’s a lot like a photography studio. You’ve got all these different ways to manipulate the light to get the desired effect. Can you give me an example of how these techniques might be used in a real world application? Sure. Let’s say you’re inspecting a bottle on a production line and you want to check the fill level. Backlighting would be a great choice there because it creates a clear outline of the liquid, which makes it easy for the system to measure how full the bottle is. Gotcha. OK. On the other hand,
If you want to inspect the surface of the bottle for scratches or defects, diffuse lighting might be a better choice because it’ll minimize the glare and highlight those imperfections. So choosing the right lighting technique is all about understanding what features you want to emphasize and how light interacts with different materials. Exactly. OK, this is fascinating stuff. Before we move on, are there any other key takeaways about cameras, lenses, and lighting?
that our listeners should keep in mind. Well, I think the most important thing to remember is that there’s no one size fits all solution. know, the best choices for your camera lens and lighting are always going to depend on the specific application. But understanding the basics, like we’ve talked about today, will give you a framework for making informed decisions. Yeah, absolutely. It’s all about choosing the right tools for the job. OK, so even with the perfect setup, I imagine there are still some challenges to overcome. there are always challenges.
Right. I’ve heard of something called lens distortion. Can you explain what that is and why it matters? Sure. You know how when you look through a fisheye lens, straight lines appear curved? Yeah. Well, similar distortions can occur in machine vision lenses, especially at the edges of the image. And that can really throw off measurements and make it difficult for the system to accurately interpret what it’s seeing. So a square might not actually look
perfectly square to the camera, which seems like a big problem. It can be huge problem if you’re trying to measure things precisely. OK, so how do we fix that? That’s where checkerboard calibration comes in. OK. So basically, you take pictures of a precisely patterned checkerboard with your camera and lens setup. OK. And then you use software to analyze those images and identify and correct for any distortion that’s present in the lens. So it’s like teaching the system to see straight.
Exactly. By understanding the distortion pattern, the software can then unwarp the image and essentially correct for the lens’s imperfections. That’s awesome. It’s like magic, but it’s really just clever engineering. That’s right. So speaking of clever engineering, another topic that comes up a lot in machine vision is the trade-off between resolution and speed. yeah, that’s a classic challenge. Can you break that down for us? Sure.
Higher resolution cameras capture more detail, but they also generate larger images which take longer to process. Lower resolution cameras sacrifice some detail, but they can operate at much faster speeds. So it’s all about finding the right balance for your application. So how do you know which way to lean? Well, it really depends on your specific needs. You know, if you’re inspecting for tiny defects,
on a fast moving production line, speed might be more important than resolution. Because you need to keep up with the flow of products. But if you’re analyzing detailed images in a more controlled environment, resolution might be the more critical factor. It sounds like there’s a lot to consider when setting up a machine vision system. There is. Are there any general tips or best practices you can share that might help our listeners make informed decisions? Absolutely.
One of the best things you can do is clearly define your goals and requirements upfront. Like what are you actually trying to achieve with this machine vision system? What level of precision do you need? How fast does it need to operate? So start with the why. Exactly. Before you worry about the how. Exactly. Once you have a clear understanding of your needs, then you can start thinking about the specific components and techniques that’ll help you meet those requirements. don’t be afraid to experiment. know, machine vision is a field where
hands-on experience can be invaluable. totally agree with that. OK. So let’s put all this knowledge to the test. OK. Let’s say our listener is tasked with setting up a machine vision system to inspect a batch of shiny curved metal parts for tiny surface defects. What are some key considerations they should keep in mind? All right. So first off, they’re going to need to think about how to minimize glare from those shiny surfaces. Right. Because those are going to reflect light like crazy. Exactly.
So diffuse lighting would probably be the best choice here. Okay. Provides even illumination and reduces those harsh reflections. Gotcha. So we don’t want the camera getting tricked by all that shine. Exactly. Okay. What about the camera itself? Color or monochrome? I would go with monochrome in this case. Since we’re not really concerned with color variations and monochrome sensors are more sensitive to light. Right. Which is helpful for detecting small details. Okay. Plus they offer faster processing speed.
Okay, so we’re prioritizing sensitivity and speed here. What about the lens? Any recommendations there? Yeah, given the curved surface of the part’s lens distortion is going to be a key consideration. Okay. So you want a lens with minimal distortion, especially towards the edges of the image. Right. That’s crucial for accurate analysis. Okay. And of course, checkerboard calibration. absolutely. To correct for any imperfections. Can’t forget about that. Gotcha. Okay, so now the age-old question.
Resolution or speed, which one wins in this scenario? Well, since we’re looking for tiny defects, resolution is going to be the deciding factor here. OK. They’ll want a camera with enough resolution to clearly capture those minute flaws, which might mean sacrificing some speed. Right. But in a controlled inspection environment like this, that’s probably a trade-off worth considering. Any final words of wisdom for our listeners as they embark on their machine vision journey?
Just remember the best machine vision system is the one that’s tailored to your specific needs. Don’t be afraid to ask questions, experiment, and learn from your experiences. All so before we wrap up, I want to circle back to something we talked about earlier. You mentioned that monochrome sensors are more sensitive to light, which makes them.
Great for detecting small details. Can you explain exactly how that works? Sure. So basically monochrome sensors don’t have color filters, which means that they can capture more of the available light. I see. And that extra light translates into a stronger signal, which makes it easier to distinguish between small variations in intensity. So it’s all about maximizing the amount of light that reaches the sensor. Exactly. OK, that makes sense. So to recap, we’ve talked about
the importance of choosing the right camera lens and lighting for your specific application. Right. We’ve discussed the challenges of lens distortion and how to overcome them with checkerboard calibration. Uh-huh. And we’ve explored the trade-offs between resolution and speed. Yep. It’s been a really comprehensive overview. It has. And I think it’s given our listeners a solid foundation for understanding the fundamentals of machine vision. Absolutely. So until next time, folks, keep those cameras rolling, those insights flowing.
Get a quote in less than 24 hours
