Article: A YOLO11-based lightweight traffic sign detection model Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.28 pp.73 - 90 Abstract: This paper introduces a lightweight enhanced model based on YOLO11 to tackle the dual challenges of reducing model size and boosting precision for deploying traffic sign detection systems on autonomous driving mobile devices. Initially, the backbone network integrates the C3G2 module, which effectively minimises parameters while strengthening multi-scale feature extraction. Subsequently, a parameter-free attention mechanism, Tr-simAM, is developed to lower computational load and heighten sensitivity to small targets. Finally, the neck network incorporates the lightweight dynamic upsampling module DySample to enhance feature fusion and localisation precision. Experimental outcomes reveal that the refined algorithm achieves an 84% precision rate, signifying a 1.2% gain over the baseline YOLOv11n model, while model parameters and computational resources decrease by 19% and 7%, respectively. These lightweight enhancements efficiently fulfil practical requirements for real-time performance and computational constraints in autonomous driving mobile devices. Inderscience Publishers - linking academia, business and industry through research

Title: A YOLO11-based lightweight traffic sign detection model

Authors: Enming Liang; Tianlei Ye; Yihong Li; Tong Zhou; Yunfei Wang; Yajie Pang

Addresses: School of Cyberspace Security, Hainan University, Haikou, 570228, China ' School of Cyberspace Security, Hainan University, Haikou, 570228, China ' School of Cyberspace Security, Hainan University, Haikou, 570228, China ' School of Cyberspace Security, Hainan University, Haikou, 570228, China ' School of Cyberspace Security, Hainan University, Haikou, 570228, China ' School of Information Technology, Hainan College of Economics and Business, Haikou, 571127, China

Abstract: This paper introduces a lightweight enhanced model based on YOLO11 to tackle the dual challenges of reducing model size and boosting precision for deploying traffic sign detection systems on autonomous driving mobile devices. Initially, the backbone network integrates the C3G2 module, which effectively minimises parameters while strengthening multi-scale feature extraction. Subsequently, a parameter-free attention mechanism, Tr-simAM, is developed to lower computational load and heighten sensitivity to small targets. Finally, the neck network incorporates the lightweight dynamic upsampling module DySample to enhance feature fusion and localisation precision. Experimental outcomes reveal that the refined algorithm achieves an 84% precision rate, signifying a 1.2% gain over the baseline YOLOv11n model, while model parameters and computational resources decrease by 19% and 7%, respectively. These lightweight enhancements efficiently fulfil practical requirements for real-time performance and computational constraints in autonomous driving mobile devices.

Keywords: YOLO11; traffic signs; object detection; lightweight network; attention mechanism; upsampling module; private dataset.

DOI: 10.1504/IJICT.2026.152550

International Journal of Information and Communication Technology, 2026 Vol.27 No.28, pp.73 - 90

Received: 13 Jan 2026
Accepted: 19 Feb 2026
Published online: 26 Mar 2026 *

Title: A YOLO11-based lightweight traffic sign detection model

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