Detecting method and device of obstacles based on disparity map and automobile driving assistance system
First Claim
1. A disparity map-based obstacle detection method, at least comprising the steps of:
- acquiring a disparity map and a V-disparity map in accordance with an image comprising a road surface;
simulating the road surface in accordance with the V-disparity map;
identifying a first obstacle in accordance with a result of simulating the road surface;
extracting an object whose disparity value is greater than a first threshold in accordance with the disparity map, and subjecting the extracted object whose disparity value is greater than the first threshold to a morphological operation so as to identify a second obstacle;
extracting an object whose disparity value is smaller than a second threshold in accordance with the disparity map, and subjecting the extracted object whose disparity value is smaller than the second threshold to a morphological operation so as to identify a third obstacle; and
screening the first obstacle, the second obstacle and the third obstacle in accordance with a training model, so as to detect a final obstacle, the training model being acquired through machine learning in accordance with correct obstacle information and erroneous obstacle information.
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Abstract
The present disclosure provides a disparity map-based obstacle detection method, a disparity map-based obstacle detection device, and a vehicle assistant driving system. The method includes steps of: acquiring a disparity map and a V disparity map in accordance with an image including a road surface; simulating the road surface in accordance with the V disparity map; identifying a first obstacle in accordance with a simulation result; extracting an object whose disparity value is greater than a first threshold in accordance with the disparity map, and subjecting the object to morphological operation so as to identify a second obstacle; extracting an object whose disparity value is smaller than a second threshold in accordance with the disparity map, and subjecting the object to morphological operation so as to identify a third obstacle; and screening the first obstacle, the second obstacle and the third obstacle in accordance with a training model, so as to detect the obstacle, the training model being acquired through machine learning in accordance with correct obstacle information and erroneous obstacle information. According to the present disclosure, it is able to improve the robustness.
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Citations
7 Claims
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1. A disparity map-based obstacle detection method, at least comprising the steps of:
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acquiring a disparity map and a V-disparity map in accordance with an image comprising a road surface; simulating the road surface in accordance with the V-disparity map; identifying a first obstacle in accordance with a result of simulating the road surface; extracting an object whose disparity value is greater than a first threshold in accordance with the disparity map, and subjecting the extracted object whose disparity value is greater than the first threshold to a morphological operation so as to identify a second obstacle; extracting an object whose disparity value is smaller than a second threshold in accordance with the disparity map, and subjecting the extracted object whose disparity value is smaller than the second threshold to a morphological operation so as to identify a third obstacle; and screening the first obstacle, the second obstacle and the third obstacle in accordance with a training model, so as to detect a final obstacle, the training model being acquired through machine learning in accordance with correct obstacle information and erroneous obstacle information. - View Dependent Claims (2, 3)
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4. A disparity map-based obstacle detection device, at least comprising:
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an acquisition unit configured to acquire a disparity map and a V-disparity map in accordance with an image including a road surface; a simulation unit configured to simulate the road surface in accordance with the V-disparity map; a first identification unit configured to identify a first obstacle in accordance with a result of simulating the road surface; a second identification unit configured to extract an object whose disparity value is greater than a first threshold in accordance with the disparity map, and subject the extracted object whose disparity value is greater than the first threshold to a morphological operation so as to identify a second obstacle; a third identification unit configured to extract an object whose disparity value is smaller than a second threshold in accordance with the disparity map, and subject with the extracted object whose disparity value is smaller than the second threshold to a morphological operation so as to identify a third obstacle; and a detection unit configured to screen the first obstacle, the second obstacle and the third obstacle in accordance with a training model, so as to detect a final obstacle, the training model being acquired through machine learning in accordance with correct obstacle information and erroneous obstacle information. - View Dependent Claims (5, 6, 7)
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Specification