计算机工程与应用2025，Vol.61Issue(14)：101-111,11.DOI:10.3778/j.issn.1002-8331.2412-0348
EMF-YOLO:轻量化多尺度特征提取路面缺陷检测算法
EMF-YOLO:Lightweight Multi-Scale Feature Extraction Algorithm for Road Surface Defect Detection
摘要
Abstract
Road surface defect detection is vital for driving safety and extending road lifespan.Existing algorithms struggle with complex backgrounds,real-time processing,and memory usage.This paper presents EMF-YOLO,an improved light-weight algorithm based on YOLOv8n,aiming to enhance detection accuracy while reducing computational and memory costs.The method introduces an enhanced feature fusion pyramid(EFFP)to optimize multi-scale feature representation,and incorporates a deformable attention mechanism(DA)to improve feature extraction in complex scenes.A multi-scale edge sensitivity enhancement(MESA)module replaces traditional C2f convolution to enhance small object detection.Additionally,a decoupled batch normalization-based shared convolution detection head(DBSCD)reduces model parame-ters and computational complexity,decreasing model size and speeding up inference.Experimental results show that EMF-YOLO achieves 89.2%detection accuracy on the RDD2022 dataset,outperforming YOLOv5n by 2 percentage points,while reducing model parameters and computation by 36.1%and 25%,respectively,balancing accuracy with light-weight performance.关键词
路面缺陷检测/YOLOv8/轻量化/多尺度特征提取/边缘敏感性增强Key words
road surface defect detection/YOLOv8/lightweight/multi-scale feature extraction/edge sensitivity enhancement分类
信息技术与安全科学引用本文复制引用
秦乐,谭泽富,雷国平,陈秋伯..EMF-YOLO:轻量化多尺度特征提取路面缺陷检测算法[J].计算机工程与应用,2025,61(14):101-111,11.基金项目
在渝高校与中科院所属院所合作项目(HZ2021012). （HZ2021012）
