Technology giants such as Google and Tesla are sparing no effort to develop autonomous driving technology, but this technology needs to break through in many key subsystems as it matures. A machine imaging recognition system developed by the University of Cambridge in the United Kingdom is expected to solve the problem of how to see and distinguish objects on the road at a lower cost.
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The system is called "segnet". By simply using the system's core application with a conventional camera or even a smartphone camera, you can quickly capture the road environment and classify different objects in the image in real time, such as roads, street signs, Pedestrians, buildings, etc., and conduct cross-reference identification.
Researchers say they have been tested on urban roads and highways, and the system's recognition accuracy is satisfactory, and performance in this area exceeds that of laser or radar-based sensors. Next, the researchers are also preparing to conduct tests in the countryside, even in snow and desert environments.
At present, prototypes of self-driving cars in development use sensors based on radar and other technologies for object recognition. However, the price of such equipment is often relatively high, even higher than the price of the vehicle, which greatly affects the practical prospects of self-driving cars.
Researchers say that compared to expensive sensors, this newly developed system is not only less expensive, but also "deep-learning." Researchers will continue to add relevant classification identification data to the system to improve recognition accuracy. Although the system is not yet mature enough to directly control vehicle travel, it has been used as a vehicle collision warning system.
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