东南大学学报（自然科学版）2026，Vol.56Issue(3)：389-398,10.DOI:10.3969/j.issn.1001-0505.2026.03.007
基于YOLOv11n的桥梁水下结构病害轻量化快速目标检测模型
Lightweight and fast object detection model for bridge underwater defects based on YOLOv11n
摘要
Abstract
To achieve efficient detection of bridge underwater defects,a lightweight and fast object detection model CF-YOLOv11n was proposed.A context-aware local enhancement attention mechanism with a dual-branch structure was introduced to fuse global semantic and local detail information,while depth-wise convolution was used to reduce the redundant computation and improve the inference speed.A frequency-modulation feed-forward network performs local-window frequency domain filtering to realize multi-scale feature modeling and suppress background interference,thereby improving the detection accuracy.The results show that the mAP0.5:0.95(mean average precision at intersection over union thresholds from 0.5 to 0.95)of CF-YOLOv11n on a real bridge dataset reaches 45.50%,which is 2.46%higher than that of the baseline.The inference speed is 66.13 frame/s,which is 2.25 times faster than that of the baseline.Com-pared with the baseline model,the proposed model can capture multi-scale information more effectively,accel-erate the inference process,and balance accuracy and speed,demonstrating stronger engineering application value in real-time detection tasks under actual underwater bridge environments.关键词
桥梁水下结构/目标检测/注意力机制/前馈神经网络/频域特征建模Key words
bridge underwater structure/object detection/attention mechanism/feedforward neural network(FNN)/frequency domain feature modeling分类
交通工程引用本文复制引用
赵井卫,沈涵,侯士通,赵鹏程,吴刚..基于YOLOv11n的桥梁水下结构病害轻量化快速目标检测模型[J].东南大学学报（自然科学版）,2026,56(3):389-398,10.基金项目
国家自然科学基金青年基金资助项目(52208306) （52208306）
江苏省自然科学基金青年基金资助项目(BK20220849). （BK20220849）
