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From RGB cameras to LiDAR sensors, explore how different types of computer vision cameras are used in various applications across different industries.
Many technical factors, such as data, algorithms, and computing power, contribute to the success of an artificial intelligence (AI) application. Specifically in computer vision, a subfield of AI that focuses on enabling machines to analyze and understand images and videos, one of the most critical factors is the input or data source: the camera. The quality and type of cameras used for a computer vision application directly affect the performance of AI models.
Choosing the right camera is crucial because different computer vision tasks require different types of visual data. For instance, high-resolution cameras are used for applications like facial recognition, where fine facial details must be captured with precision. In contrast, lower-resolution cameras can be used for tasks like queue monitoring that depend on broader patterns more than intricate details.
Nowadays, there are many types of cameras available, each designed to meet specific needs. Understanding their differences can help you optimize your computer vision innovations. Let’s explore the various types of computer vision cameras and their applications across different industries.
RGB (red, green, and blue) cameras are commonly used in computer vision applications. They capture images in the visible spectrum within wavelengths from 400 to 700 nanometers (nm). Since these images are similar to how humans see, RGB cameras are used for many tasks like object detection, instance segmentation, and pose estimation in situations where human-like vision is enough.
These tasks usually involve identifying and detecting objects from a two-dimensional (2D) perspective, where capturing depth isn’t necessary for accurate results. However, when an application requires depth information, like in 3D object detection or robotics, RGB-D (Red, Green, Blue, and Depth) cameras are used. These cameras combine RGB data with depth sensors to capture 3D details and provide real-time depth measurements.
An interesting application where RGB-D cameras can come in handy is virtual try-ons, a concept that is becoming more popular in retail stores. To put it simply, smart screens integrated with RGB-D cameras and sensors can gather details like a shopper’s height, body shape, and shoulder width. Using this information, the system can digitally overlay clothing onto a live image of the customer. Computer vision tasks, such as instance segmentation and pose estimation, can process the visual data to accurately detect the customer’s body and align the clothing to fit their proportions in real-time.
Virtual try-ons give customers a 3D view of how an outfit would fit, and some systems can even mimic how the fabric would move for a more realistic experience. Computer vision and RGB-D cameras make it possible for customers to skip the fitting room and try on clothes instantly. It saves time, makes comparing styles and sizes easier, and improves the overall shopping experience.
Stereo cameras are a type of camera that uses multiple image sensors to capture depth by comparing images from different angles. They are more accurate than single-sensor systems. Meanwhile, Time-of-Flight (ToF) cameras or sensors measure distances by emitting infrared light that bounces off objects and returns to the sensor. The time it takes for the light to return is calculated by the camera's processor to determine the distance.
In some cases, stereo cameras are integrated with ToF sensors, combining the strengths of both devices to capture depth information quickly and with high precision. The combination of a ToF sensor's real-time distance measurements with a stereo camera's detailed depth perception makes it ideal for applications like autonomous vehicles and consumer electronics, where both speed and accuracy are vital.
It’s possible that you may have used a Time-of-Flight (ToF) camera without even realizing it. In fact, popular smartphones from brands like Samsung, Huawei, and Realme often include ToF sensors to enhance depth-sensing capabilities. The precise depth information these cameras provide is used to create the popular bokeh effect, where the background is blurred while the subject remains in sharp focus.
ToF sensors are also becoming essential for other applications beyond photography, such as gesture recognition and augmented reality (AR). For example, phones like the Samsung Galaxy S20 Ultra and Huawei P30 Pro use these sensors to map out 3D depth in real-time, improving both photography and interactive experiences.
Thermal cameras, as the name suggests, are widely used for heat detection in various applications, including manufacturing industries and automobile factories. These cameras measure temperature and can be used to alert users when they detect critical levels of heat that are either too high or too low. By detecting infrared radiation, which is invisible to the human eye, they provide precise temperature readings. Often referred to as infrared cameras, their uses also extend beyond industrial settings. For instance, thermal cameras are also used in agriculture to monitor livestock health, in building inspections to identify heat leaks, and in firefighting to locate hotspots.
Machines and electrical systems at manufacturing plants or oil and gas rigs often operate continuously and generate heat as a byproduct. Over time, excessive heat buildup can occur in components such as motors, bearings, or electrical circuits, potentially leading to equipment failure or safety hazards.
Thermal cameras can help operators monitor these systems by detecting abnormal temperature spikes early. An overheating motor can be scheduled for maintenance and to prevent costly breakdowns. By integrating thermal imaging into regular inspections, industries can implement predictive maintenance, reduce downtime, extend equipment life, and ensure a safer work environment. Overall, plant performance can be improved, and the risk of unexpected failures can be minimized.
High-speed cameras are designed to capture more than 10,000 frames per second (FPS) so that they can process rapid movements with exceptional accuracy. For example, when products move quickly on a production line, high-speed cameras can be used to monitor them and detect any abnormalities.
On the other hand, slow-motion cameras can be used to capture footage at high frame rates and then reduce the playback speed. This enables viewers to observe details often missed in real-time. These cameras are used to evaluate the performance of firearms and explosive materials. The ability to slow down and analyze intricate movements is ideal for this type of application.
In certain situations, combining high-speed and slow-motion cameras can help with the detailed analysis of fast and slow-moving objects within the same event. Let’s say, we are analyzing a game of golf. High-speed cameras can measure the speed of a golf ball, while slow-motion cameras can analyze a golfer's swing movements and body control.
Multispectral cameras are specialized devices that can record multiple wavelengths of the light spectrum, including ultraviolet and infrared, in a single shot. Multispectral imaging provides valuable detailed data that traditional cameras cannot capture. Similar to hyperspectral cameras, which capture even more narrow and continuous bands of light, multispectral cameras are used in fields like agriculture, geology, environmental monitoring, and medical imaging. For example, in healthcare, multispectral cameras can help visualize different tissues by capturing images across multiple wavelengths.
Similarly, drones equipped with multispectral imaging are making significant strides in agriculture. They can identify unhealthy plants or those affected by insects and pests at an early stage. These cameras can analyze the near-infrared spectrum, and healthy plants generally reflect more near-infrared light than their unhealthy counterparts. By adopting such AI techniques in agriculture, farmers can implement countermeasures early to boost yield and reduce crop loss.
LiDAR (Light Detection and Ranging) cameras use laser pulses to create 3D maps and detect objects from a distance. They're effective in many conditions like fog, rain, darkness, and high temperatures, though heavy weather such as rain or fog can impact their performance. LiDAR is commonly used in applications like self-driving cars for navigation and obstacle detection.
LiDAR acts like the car's eyes, sending out laser pulses and measuring how long they take to bounce back. These insights help the car calculate distances and identify objects like cars, pedestrians, and traffic signals, providing a 360-degree view for safer driving.
When it comes to computer vision, cameras serve as the eyes that allow machines to see and interpret the world similarly to how humans do. Choosing the right type of camera is key to the success of different computer vision applications. From standard RGB cameras to advanced LiDAR systems, each type offers unique features suited to specific tasks. By understanding the variety of camera technologies and their uses, developers, and researchers can better optimize computer vision models to tackle complex real-world challenges.
Explore more about AI by checking out our GitHub repository. Join our community to connect with other like-minded Vision AI enthusiasts. Learn more about computer vision applications in healthcare and manufacturing on our solution pages.
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