Year: 2024
Type: conference-paper
Abstract: Based on YOLOv8 algorithm, this paper improves the loss function and adds a simple attention mechanism to study obstacle recognition and localization in autonomous driving. The improved YOLOv8 model achieved an average accuracy of 92.9% and an average recall of 88.7%, while the recognition speed is 62.7 frames per second. The SGM (Semi Global Block Matching) stereo matching algorithm is used to achieve three-dimensional spatial positioning of roadblocks. The relative error of achieved localization is within 1.1%, which can basically meet the visual system requirements for obstacle positioning in autonomous driving scenarios.
Authors: Zhaomin Zhu, Jinzhan Wei, Haoyue Xue
Institution: Guilin University of Aerospace Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4405676628