Method of classifying a condition of a road surface
AssigneeGM Global Technology Operations LLC
InventorsWei Tong, Qingrong Zhao, Shuqing Zeng, Bakhtiar B. Litkouhi
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Abstract
A method of identifying a condition of a road surface includes capturing at least a first image of the road surface with a first camera, and a second image of the road surface with a second camera. The first image and the second image are tiled together to form a combined tile image. A feature vector is extracted from the combined tile image using a convolutional neural network, and a condition of the road surface is determined from the feature vector using a classifier.
Claims19
A method of identifying a condition of a road surface, the method comprising:
- capturing a first image of the road surface with a camera;
- capturing a second image of the road surface with the camera;
- tiling the first image and the second image together to form a combined tile image;
- extracting a feature vector from the combined tile image;
- and
- determining a condition of the road surface from the feature vector with a classifier.
A method of identifying a condition of a road surface, the method comprising:
- capturing a first image of the road surface with a first camera, wherein the first image is actively illuminated by a light source;
- capturing a second image of the road surface with a second camera, wherein the second image is passively illuminated by ambient light, and is an image of the road surface in a wheel splash region of a vehicle;
- capturing a third image of the road surface with a third camera, wherein the third image is passively illuminated by ambient light, and is an image of the road surface in a region close to a side of the vehicle;
- tiling the first image, the second image, and the third image together to form a combined tile image;
- extracting a feature vector from the combined tile image with a convolutional neural network;
- and
- determining a condition of the road surface from the feature vector with a classifier.
A vehicle comprising:
- a body;
- at least one camera attached to the body and positioned to capture an image of a road surface in a first region relative to the body, and an image of the road surface in a second region relative to the body;
- a light source attached to the body and positioned to illuminate the road surface in the first region;
- a computing unit having a processor, a convolutional neural network, a classifier, and a memory having a road surface condition algorithm saved thereon, wherein the processor is operable to execute the road surface condition algorithm to:
- capture a first image of the road surface in the first region with the at least one camera, with the first image actively illuminated by the light source;
- capture a second image of the road surface in the second region with the at least one camera;
- tile the first image and the second image together to form a combined tile image;
- extract a feature vector from the combined tile image with the convolutional neural network;
- and
- determine a condition of the road surface from the feature vector with the classifier.
Dependencies are inferred from the claim text.