Encompass obstacle image detection method based on U-V disparity map and RANSAC algorithm

With the rapid development of autonomous driving technology, obstacle image detection has become an important problem that autonomous vehicles must solve. Obstacle image detection accuracy directly affects the safety and reliability of autonomous vehicles. Currently, these methods often face issues...

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Vydané v:Scientific reports Ročník 15; číslo 1; s. 6164 - 18
Hlavný autor: Xu, Huiqiong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 20.02.2025
Nature Publishing Group
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639/705/117
639/705/531
639/705/794
Algorithms
Autonomous driving
Autonomous vehicles
Humanities and Social Sciences
Image recognition
multidisciplinary
Obstacle detection
Random sampling consistency algorithm
Science
Science (multidisciplinary)
Statistical sampling
U-V disparity map
Random sampling consistency algorithm
Obstacle detection
Image recognition
Autonomous driving
U-V disparity map
ISSN:2045-2322, 2045-2322
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Abstract With the rapid development of autonomous driving technology, obstacle image detection has become an important problem that autonomous vehicles must solve. Obstacle image detection accuracy directly affects the safety and reliability of autonomous vehicles. Currently, these methods often face issues such as sensitivity to lighting and weather conditions. In response to these problems, research has been conducted to combine U-V disparity maps for obstacle detection. This map is used for coarse filtering of non-road disparity and finding disparity coordinates and other information for each line segment in the disparity map based on projection information. Then, a random sampling consistency algorithm is combined to perform road line fitting and remove noise. Finally, a new obstacle image detection method is designed. The results showed that the classification loss value was 0.013, the generalized intersection to union ratio loss was 0.0072, the target loss converged to 0.0026, and the accuracy of the algorithm reached over 95%. The findings of this study offer novel insights into the advancement of obstacle image detection technology, with potential applications in autonomous driving and image recognition.
AbstractList With the rapid development of autonomous driving technology, obstacle image detection has become an important problem that autonomous vehicles must solve. Obstacle image detection accuracy directly affects the safety and reliability of autonomous vehicles. Currently, these methods often face issues such as sensitivity to lighting and weather conditions. In response to these problems, research has been conducted to combine U-V disparity maps for obstacle detection. This map is used for coarse filtering of non-road disparity and finding disparity coordinates and other information for each line segment in the disparity map based on projection information. Then, a random sampling consistency algorithm is combined to perform road line fitting and remove noise. Finally, a new obstacle image detection method is designed. The results showed that the classification loss value was 0.013, the generalized intersection to union ratio loss was 0.0072, the target loss converged to 0.0026, and the accuracy of the algorithm reached over 95%. The findings of this study offer novel insights into the advancement of obstacle image detection technology, with potential applications in autonomous driving and image recognition.
Abstract With the rapid development of autonomous driving technology, obstacle image detection has become an important problem that autonomous vehicles must solve. Obstacle image detection accuracy directly affects the safety and reliability of autonomous vehicles. Currently, these methods often face issues such as sensitivity to lighting and weather conditions. In response to these problems, research has been conducted to combine U-V disparity maps for obstacle detection. This map is used for coarse filtering of non-road disparity and finding disparity coordinates and other information for each line segment in the disparity map based on projection information. Then, a random sampling consistency algorithm is combined to perform road line fitting and remove noise. Finally, a new obstacle image detection method is designed. The results showed that the classification loss value was 0.013, the generalized intersection to union ratio loss was 0.0072, the target loss converged to 0.0026, and the accuracy of the algorithm reached over 95%. The findings of this study offer novel insights into the advancement of obstacle image detection technology, with potential applications in autonomous driving and image recognition.
With the rapid development of autonomous driving technology, obstacle image detection has become an important problem that autonomous vehicles must solve. Obstacle image detection accuracy directly affects the safety and reliability of autonomous vehicles. Currently, these methods often face issues such as sensitivity to lighting and weather conditions. In response to these problems, research has been conducted to combine U-V disparity maps for obstacle detection. This map is used for coarse filtering of non-road disparity and finding disparity coordinates and other information for each line segment in the disparity map based on projection information. Then, a random sampling consistency algorithm is combined to perform road line fitting and remove noise. Finally, a new obstacle image detection method is designed. The results showed that the classification loss value was 0.013, the generalized intersection to union ratio loss was 0.0072, the target loss converged to 0.0026, and the accuracy of the algorithm reached over 95%. The findings of this study offer novel insights into the advancement of obstacle image detection technology, with potential applications in autonomous driving and image recognition.With the rapid development of autonomous driving technology, obstacle image detection has become an important problem that autonomous vehicles must solve. Obstacle image detection accuracy directly affects the safety and reliability of autonomous vehicles. Currently, these methods often face issues such as sensitivity to lighting and weather conditions. In response to these problems, research has been conducted to combine U-V disparity maps for obstacle detection. This map is used for coarse filtering of non-road disparity and finding disparity coordinates and other information for each line segment in the disparity map based on projection information. Then, a random sampling consistency algorithm is combined to perform road line fitting and remove noise. Finally, a new obstacle image detection method is designed. The results showed that the classification loss value was 0.013, the generalized intersection to union ratio loss was 0.0072, the target loss converged to 0.0026, and the accuracy of the algorithm reached over 95%. The findings of this study offer novel insights into the advancement of obstacle image detection technology, with potential applications in autonomous driving and image recognition.
ArticleNumber 6164
Author Xu, Huiqiong
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U-V disparity map
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Abstract With the rapid development of autonomous driving technology, obstacle image detection has become an important problem that autonomous vehicles must...
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Random sampling consistency algorithm
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