{"title":"A Road Damage Detection Model Based on Improved RT-DETR for Complex Environments","authors":"Xianglong Luo;Ruchen Liu;Xibin He;Huijie Wang","doi":"10.1109/TIM.2025.3582316","DOIUrl":null,"url":null,"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 <uri>https://github.com/Lrc-1109/MMR-DETR</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6000,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11048592/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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
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.