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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 6164 - 18
1. Verfasser: Xu, Huiqiong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 20.02.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-89785-5