Analysis of Improved Logo Detection Performance of YOLOv11 in Autonomous Driving Environment

With the rapid development of autonomous driving technology, traffic sign detection, as a core component of environmental perception systems, faces challenges such as small target missed detection, complex background interference, and real-time requirements. Based on YOLOv11 model, this paper proposes an optimization scheme for traffic sign detection in the automatic driving scene, which effectively improves the detection accuracy of small targets and provides a new solution for the sign detection of the auto drive system.

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Publication Details

Journal
Applied and Computational Engineering
Published
2025-08-13
DOI
https://doi.org/10.54254/2755-2721/2025.25898
Primary Topic
Advanced Neural Network Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Analysis of Improved Logo Detection Performance of YOLOv11 in Autonomous Driving Environment

Jianfei Jiang
Applied and Computational Engineering
Advanced Neural Network Applications
article

Analysis of Improved Logo Detection Performance of YOLOv11 in Autonomous Driving Environment

Jianfei Jiang
article en

Abstract

With the rapid development of autonomous driving technology, traffic sign detection, as a core component of environmental perception systems, faces challenges such as small target missed detection, complex background interference, and real-time requirements. Based on YOLOv11 model, this paper proposes an optimization scheme for traffic sign detection in the automatic driving scene, which effectively improves the detection accuracy of small targets and provides a new solution for the sign detection of the auto drive system.

Applied and Computational EngineeringVol. 173(1)
Queensland University of Technology (AU)
Openalex Percentile: Top 81%
Advanced Neural Network Applications
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