OBSTACLE DETECTION METHOD AND APPARATUS, DEVICE, AND MEDIUM
This application discloses an obstacle detection method, including: obtaining a first image, where the first image is an image encoded based on an RGB model; reconstructing the first image to obtain a second image, where the second image is a hyper spectral image; and extracting a hyper spectral feature from the hyper spectral image, and classifying a candidate object in the hyper spectral image based on the hyper spectral feature to obtain an obstacle detection result. Because different textures correspond to different hyper spectral features, classifying candidate objects in hyper spectral images based on the hyper spectral features can distinguish an object that has a similar color but a different texture.
1 . An obstacle detection method, wherein the method comprises:
obtaining a first image, wherein the first image is an image encoded based on an RGB model;
reconstructing the first image to obtain a second image; and
extracting a hyper spectral feature from the hyper spectral image, and
classifying a candidate object in the hyper spectral image based on the hyper spectral feature to obtain an obstacle detection result.
2 . The method according to claim 1 , wherein the reconstructing the first image to obtain a second image comprises:
extracting a spatial feature of the first image; and
performing image reconstruction based on the spatial feature of the first image by using a correspondence between the spatial feature and a spectral feature to obtain the second image.
3 . The method according to claim 1 , wherein the method further comprises:
obtaining a data dictionary from a configuration file, wherein the data dictionary comprises a correspondence between a spatial feature and a spectral feature; or
obtaining sample data, and performing machine learning by using the sample data to obtain a correspondence between a spatial feature and a spectral feature.
4 . The method according to claim 1 , wherein the method further comprises:
fusing the hyper spectral feature and the spatial feature of the first image to obtain a fused feature; and
the classifying a candidate object in the hyper spectral image based on the hyper spectral feature comprises:
classifying the candidate object in the hyper spectral image based on the fused feature.
5 . The method according to claim 4 , wherein the hyper spectral feature and the spatial feature of the first image are fused by using a Bayesian data fusion algorithm.
6 . The method according to claim 1 , wherein the first image comprises an RGB image, an RCCC image, an RCCB image, or an RGGB image.
7 . The method according to claim 1 , wherein the obstacle detection result comprises a location and a texture of the obstacle; and
the method further comprises:
determining a drivable area based on the location and the texture of the obstacle; and
sending the drivable area to a controller of a vehicle to indicate the vehicle to travel based on the drivable area.
8 . An obstacle detection apparatus, comprising:
one or more processors, and
a non-transitory storage medium in communication with the one or more processors, the non-transitory storage medium configured to store program instructions, wherein, when executed by the one or more processors, the instructions cause the apparatus to perform operations, the operations comprising:
obtaining a first image;
reconstructing the first image to obtain a second image, wherein the second image is a hyper spectral image; and
extracting a hyper spectral feature from the hyper spectral image, and classifying a candidate object in the hyper spectral image based on the hyper spectral feature to obtain an obstacle detection result.
9 . A computer-readable storage medium, wherein the computer-readable storage medium is configured to store a computer program, and the computer program is configured to perform the obstacle detection method comprising:
obtaining a first image;
reconstructing the first image to obtain a second image, wherein the second image is a hyper spectral image; and
extracting a hyper spectral feature from the hyper spectral image, and classifying a candidate object in the hyper spectral image based on the hyper spectral feature to obtain an obstacle detection result.
10 . The method of claim 1 , wherein the second image is a hyper spectral image.
11 . The obstacle detection apparatus according to claim 8 , the operations further comprising:
extracting a spatial feature of the first image; and
performing image reconstruction based on the spatial feature of the first image by using a correspondence between the spatial feature and a spectral feature to obtain the second image.
12 . The obstacle detection apparatus according to claim 8 , the operations further comprising:
obtaining a data dictionary from a configuration file, wherein the data dictionary comprises a correspondence between a spatial feature and a spectral feature; or
obtaining sample data, and performing machine learning by using the sample data to obtain a correspondence between a spatial feature and a spectral feature.
13 . The obstacle detection apparatus according to claim 8 , the operations further comprising:
fusing the hyper spectral feature and the spatial feature of the first image to obtain a fused feature; and
the classifying a candidate object in the hyper spectral image based on the hyper spectral feature comprises:
classifying the candidate object in the hyper spectral image based on the fused feature.
14 . The obstacle detection apparatus according to claim 13 , wherein the hyper spectral feature and the spatial feature of the first image are fused by using a Bayesian data fusion algorithm.
15 . The obstacle detection apparatus according to claim 8 , wherein the first image comprises an RGB image, an RCCC image, an RCCB image, or an RGGB image.
16 . The obstacle detection apparatus according to claim 8 , wherein the first image is an image encoded based on an RGB model.
17 . The computer-readable storage medium according to claim 9 , wherein the obstacle detection method further comprises:
extracting a spatial feature of the first image; and
performing image reconstruction based on the spatial feature of the first image by using a correspondence between the spatial feature and a spectral feature to obtain the second image.
18 . The computer-readable storage medium according to claim 9 , wherein the obstacle detection method further comprises:
obtaining a data dictionary from a configuration file, wherein the data dictionary comprises a correspondence between a spatial feature and a spectral feature; or
obtaining sample data, and performing machine learning by using the sample data to obtain a correspondence between a spatial feature and a spectral feature.
19 . The computer-readable storage medium according to claim 9 , wherein the obstacle detection method further comprises:
fusing the hyper spectral feature and the spatial feature of the first image to obtain a fused feature; and
the classifying a candidate object in the hyper spectral image based on the hyper spectral feature comprises:
classifying the candidate object in the hyper spectral image based on the fused feature.
20 . The computer-readable storage medium according to claim 9 , wherein the first image is an image encoded based on an RGB model.