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文章摘要
基于改进 RT-DETR的路面坑槽检测模型
Pavement pothole detection model based on improved RT-DETR
  
DOI:
中文关键词: 目标检测  路面坑槽  改进 RT-DETR  渐进特征金字塔网络  结构重参数化
英文关键词: object detection  pavement pothole  improved RT-DETR  asymptotic feature pyramid network  structure re-parameterization
基金项目:国家重点研发计划项目( 2022YFE0125200);国家自然科学基金项目( 51975426);湖北省重点研发计划项目( 2021BAA018, 2022BAA062).
作者单位E-mail
许小伟 武汉科技大学汽车与交通工程学院湖北武汉 430065 xuxiaowei@wust.edu.cn 
陈燕玲 武汉科技大学汽车与交通工程学院湖北武汉 430065  
占柳 武汉科技大学汽车与交通工程学院湖北武汉 430065  
漆庆华 武汉科技大学汽车与交通工程学院湖北武汉 430065  
邓明星 武汉科技大学汽车与交通工程学院湖北武汉 430065 ycdmx@126.com 
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中文摘要:
      路面坑槽对驾驶的舒适性和安全性有很大影响。针对路面图像中坑槽尺寸小和特征信息匮乏导致检测精度低的问题,提出一种基于 RT-DETR的路面坑槽检测模型 PavementPothole-DETR(PP-DETR)。其主干网络使用 SPDRSFE模块进行特征提取,可保留更多特征信息,提高小目标检测精度;引入渐进特征金字塔网络实现特征融合,避免多级传输造成的信息丢失,以解决坑槽特征信息主要集中在中、底特征层的问题;使用结构重参数化模块 Conv3XCC3进行特征再提取,在提高网络表达能力的同时又不增加计算量。实验结果显示,相比原 RT-DETR模型, PP-DETR的精确率与召回率分别提升了 2.9和 5.4个百分点, mAP达到 76.9%。本文提出的改进方法有效提升了网络的特征提取和特征融合能力,在路面坑槽检测任务上的表现明显优于 YOLO系列模型。
英文摘要:
      Pavement potholes have a great impact on driving comfort and safety. To address the issue of low detection precision caused by small pothole size and lack of feature information in road surface images,a pothole detection model based on RT-DETR,named Pavement Pothole-DETR(PP-DETR),was proposed. PP-DETR took SPDRSFE module as the backbone network for feature extraction,which can retain more feature information and improve the detection precision for small targets.PP-DETR also introduced an asymptotic feature pyramid network for feature fusion,so as to avoid information loss due to multi-level transmission and solve the problem that the pothole feature information is mainly concentrated in the middle and bottom feature layers. By using structure re-parameterization module Conv3XCC3 for feature re-extraction,PP-DETR enhanced its network expressive ability without increasing the computational load. Experimental results show that,compared with the original RT-DETR model,PP-DETR’s precision and recall rate increase by 2.9 and 5.4 percentage points,respectively,and the mAP reaches 76.9%. The proposed method effectively improves the capability of feature extraction and feature fusion of the network,significantly outperforming the YOLO series models in pavement pothole detection tasks.
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