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基于改进YOLO11的高速路面裂缝分割算法

曹霆 刘干 王朋辉 杨龙

无线电工程2026,Vol.56Issue(2):253-261,9.
无线电工程2026,Vol.56Issue(2):253-261,9.DOI:10.3969/j.issn.1003-3106.2026.02.007

基于改进YOLO11的高速路面裂缝分割算法

Highway Pavement Crack Segmentation Algorithm Based on Improved YOLO11

曹霆 1刘干 1王朋辉 2杨龙3

作者信息

  • 1. 西安理工大学计算机科学与工程学院,陕西 西安 710048
  • 2. 长安大学道路施工技术与装备教育部重点实验室,陕西 西安 710064
  • 3. 西安电子科技大学通信工程学院,陕西 西安 710071
  • 折叠

摘要

Abstract

To address the challenges including the high missed detection rates,insufficient boundary localization accuracy,and poor adaptability to complex environments in the high-speed pavement crack segmentation tasks under the IoT edge environment,an improved YOLO11 segmentation model is proposed.This model integrates lightweight multi-scale feature enhancement and a Polarized Self-Attention(PSA)mechanism to achieve a synergistic optimization between high precision and low latency.A lightweight design of the Atrous Spatial Pyramid Pooling(ASPP)module is introduced,which employs parallel atrous convolutions with different dilation rates and global average pooling to enhance multi-scale contextual information extraction capability while reducing computational complexity.Meanwhile,an optimized PSA mechanism is incorporated to jointly recalibrate features across channel and spatial dimensions,effectively suppressing complex background interference and improving crack edge recognition accuracy.This mechanism enables edge nodes to transmit only sparse features filtered by attention weights,thereby reducing data transmission overhead and accommodating multi-node collaborative perception in the IoT environment.Experiments on the self-built crack dataset from the G85 highway demonstrate that the improved model exhibits strong robustness under complex backgrounds and low-contrast conditions.Its overall performance has improved by 5.62%compared to the YOLO11n baseline,and it has also significantly outperformed the state-of-the-art methods.The proposed model provides a high-precision,lightweight segmentation solution for intelligent highway pavement maintenance.

关键词

路面裂缝分割/多尺度特征融合/极化自注意力机制/YOLO11

Key words

pavement crack segmentation/multi-scale feature fusion/PSA mechanism/YOLO11

分类

信息技术与安全科学

引用本文复制引用

曹霆,刘干,王朋辉,杨龙..基于改进YOLO11的高速路面裂缝分割算法[J].无线电工程,2026,56(2):253-261,9.

基金项目

道路施工技术与装备教育部重点实验室(长安大学)开放基金(300102252510) (长安大学)

陕西省重点研发计划(2025CY-YBXM-014) (2025CY-YBXM-014)

西安市科技局重点产业链(25ZDLYB00012) Open Fund of Key Laboratory of Road Construction Technology and Equipment of Ministry of Education(Chang'an University)(300102252510) (25ZDLYB00012)

Key Research and Development Plan of Shaanxi Province(2025CY-YBXM-014) (2025CY-YBXM-014)

Key Industry Chain Project of Science and Technol-ogy Bureau in Xi'an City(25ZDLYB00012) (25ZDLYB00012)

无线电工程

1003-3106

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