English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Road obstacle detection is an important component of the advanced driver assistance system, and to improve the speed and accuracy of road obstacle detection method is a vital task. In this article, fast image region-matching method based on the maximally stable extremal regions method is proposed to improve the speed of image matching. The theoretical feasibility of detection method combining monocular camera with inertial measurement unit (IMU) is clarified. The fast road obstacle detection method based on maximally stable extremal regions combining fast image region-matching method based on maximally stable extremal regions and the vision-IMU-based obstacle detection method is proposed to bypass obstacle classification and to reduce time and space complexity for road environment perception. The AdaBoost cascade detector, the speeded-up robust features-based obstacle detection method, and the proposed method are used to detect obstacles in outdoor contrast tests. Test results show that the proposed method has higher accuracy, and the reason of high accuracy is analyzed. The processing time of AdaBoost cascade detector, speeded-up robust features-based obstacle detection method, and proposed method are compared, and the results show that the proposed method has faster processing speed, and the reason of faster processing speed is analyzed.

Fast road obstacle detection method based on maximally stable extremal regions