A Road Damage Detection Model Based on Improved RT-DETR for Complex Environments

IF 5.6 2区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xianglong Luo;Ruchen Liu;Xibin He;Huijie Wang
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引用次数: 0

Abstract

Road damage detection is crucial for maintenance and management. Timely and accurate detection improves traffic safety and extends the road service life. However, road damage in complex backgrounds is often characterized by large aspect ratios, multiple scales, and abrupt changes in direction, which greatly reduce detection a ccuracy. To solve the problem, this article proposes MMR-DETR, a road damage detection network with cross fusion of multiscale information. Specifically, for the multiscale and large aspect ratio of damage in complex backgrounds, the encoder introduces multiscale multihead self-attention (M2SA) and multiscale cross fusion (MCF) modules to learn damage information, enhancing feature representation and detection performance. Additionally, a redundant bounding box merging (RBBM) method is applied to improve localization accuracy by optimizing detection boxes. To evaluate the effectiveness of the proposed model, we conducted experiments on the UAPD, UAVPDD-2023, and RDD2022 datasets. The experimental results show that our model outperforms the existing models in terms of accuracy, recall, and mAP@0.5, exhibiting excellent detection performance and generalization. The code is available at https://github.com/Lrc-1109/MMR-DETR
基于改进RT-DETR的复杂环境下道路损伤检测模型
道路损伤检测对于维护和管理至关重要。及时准确的检测提高了交通安全,延长了道路使用寿命。然而,复杂背景下的道路损伤通常具有大宽高比、多尺度和方向突变等特征,这大大降低了检测精度。为了解决这一问题,本文提出了一种多尺度信息交叉融合的道路损伤检测网络MMR-DETR。具体而言,针对复杂背景下的多尺度、大纵横比损伤,编码器引入了多尺度多头自关注(M2SA)和多尺度交叉融合(MCF)模块来学习损伤信息,提高了特征表示和检测性能。此外,采用冗余边界盒合并(RBBM)方法,通过优化检测盒来提高定位精度。为了评估该模型的有效性,我们在UAPD、UAVPDD-2023和RDD2022数据集上进行了实验。实验结果表明,我们的模型在准确率、召回率和mAP@0.5方面都优于现有的模型,具有良好的检测性能和泛化能力。代码可在https://github.com/Lrc-1109/MMR-DETR上获得
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来源期刊
IEEE Transactions on Instrumentation and Measurement
IEEE Transactions on Instrumentation and Measurement 工程技术-工程:电子与电气
CiteScore
9.00
自引率
23.20%
发文量
1294
审稿时长
3.9 months
期刊介绍: Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.
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