Are high-resolution images necessarily suitable for machine vision?

Are high-resolution images necessarily suitable for machine vision? For the eyes of cutting-edge machines responsible for safety and security, even the sharpest two-dimensional (2D) image data is not enough for them to work in place of humans. For self-driving cars and drones, it is necessary to accurately identify the braking moment during high-speed driving; for facial recognition devices, it is necessary to accurately scan faces instead of flat images; for AR devices, real-time Large space scans for augmented reality. These machines all require not only 2D image data, but also 3D technical support, such as 3D depth cameras.
With the cooperation of eyes and brain, people can see objects stereoscopically and recognize depth and distance. Through a similar mechanism, machines can also identify multi-dimensional objects and measure distances through triangulation. For example, stereo vision uses two cameras and a processor to achieve the recognition effect. However, such mechanisms also suffer from drawbacks such as computational complexity, lack of accuracy in measuring plane distances, and low accuracy in relatively dark places, which narrow the scope of such mechanisms. As an alternative method to overcome these shortcomings, tof camera has been widely used in many scenarios. A tof camera is a simple way to measure distance by calculating the time it takes for light to bounce off an object. This method is easy and fast to run, and has the added advantage of accurately measuring distances regardless of the lighting environment because it uses a separate light source.DOMI has a variety of tof camera products suitable for machine vision, welcome to consult.

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