Vision-based rain detection using deep learning
First Claim
Patent Images
1. A method comprising:
- obtaining multiple images, each known to photographically depict a “
rain”
or a “
no rain”
condition;
training, by an artificial neural network, on the multiple images;
analyzing, by the artificial neural network after the training, a plurality of consecutive images captured by a first camera;
classifying, by the artificial neural network based on the analyzing, each image of the plurality of consecutive images as being in “
rain”
or “
no rain”
weather;
(a) determining that a major portion of the plurality of consecutive images have been classified as in “
rain”
weather; and
in response to (a), modifying operation of the vehicle.
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Abstract
A method is disclosed for using a camera on-board a vehicle to determine whether precipitation is failing near the vehicle. The method may include obtaining multiple images. Each of the multiple images may be known to photographically depict a “rain” or a “no rain” condition. An artificial neural network may be trained on the multiple images. Later, the artificial neural network may analyze one or more images captured by a first camera secured to a first vehicle. Based on that analysis, the artificial neural network may classify the first vehicle as being in “rain” or “no rain” weather.
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Citations
19 Claims
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1. A method comprising:
-
obtaining multiple images, each known to photographically depict a “
rain”
or a “
no rain”
condition;training, by an artificial neural network, on the multiple images; analyzing, by the artificial neural network after the training, a plurality of consecutive images captured by a first camera; classifying, by the artificial neural network based on the analyzing, each image of the plurality of consecutive images as being in “
rain”
or “
no rain”
weather;(a) determining that a major portion of the plurality of consecutive images have been classified as in “
rain”
weather; andin response to (a), modifying operation of the vehicle. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method comprising:
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obtaining a first plurality of images captured by one or more cameras on-board one or more vehicles, each image of the first plurality of images being known to photographically depict a “
rain”
or a “
no rain”
condition;using the first plurality of images to train an artificial neural network to distinguish between photographic data corresponding to the rain condition and photographic data corresponding to the no rain condition; analyzing, by the artificial neural network after the using, a second plurality of consecutive images captured by a first camera secured to a first vehicle; classifying, by the artificial neural network based on the analyzing, each image of the second plurality of consecutive images as being in “
rain”
or “
no rain”
weather;(a) determining that a major portion of the second plurality of consecutive images have been classified as in “
rain”
weather; andin response to (a), producing an output modifying operation of a vehicle. - View Dependent Claims (13, 14, 15, 16, 17, 18)
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19. A computer system comprising:
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one or more processors; memory operably connected to the one or more processors; and the memory storing an artificial neural network trained on a plurality of images captured by one or more cameras on-board one or more vehicles to distinguish between images corresponding to a “
rain”
condition and images corresponding to a “
no rain”
condition;a plurality of consecutive images captured by a first camera secured to a first vehicle; and software programmed to cause the one or more processors to— feed the plurality of consecutive images to the artificial neural network for classification; when a majority of the plurality of consecutive images are classified by the artificial neural network as corresponding to the “
rain”
condition thereby indicating a transition from the “
no rain”
to the “
rain”
condition, change a functional characteristic of the first vehicle.
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Specification