[1]LI Bing,WANG Yue,ZHANG Yimu,et al.Metal surface defect detection algorithm based on improved RT-DETR algorithm[J].CAAI Transactions on Intelligent Systems,2025,20(6):1404-1419.[doi:10.11992/tis.202502021]
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Metal surface defect detection algorithm based on improved RT-DETR algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 6 Page number: 1404-1419 Column: 学术论文—机器感知与模式识别 Public date: 2025-11-05
Title:
Metal surface defect detection algorithm based on improved RT-DETR algorithm
Author(s):
LI Bing1; 2;  WANG Yue1;  ZHANG Yimu1;  WEI Letao1;  XIE Zhuofan1;  YE Meng1;  ZHAI Yongjie1; 2
1. Department of Automation, North ChinaElectricPower University, Baoding 071003, China;
2. Baoding Key Laboratory of Intelligent Robot Perception and Control in Electric Power System, Baoding 071003, China
Keywords:
deep learning; metal surface defects; small target; RT-DETR; feature fusion; attention mechanism; difference convolution; object detection
CLC:
TP183
DOI:
10.11992/tis.202502021
Abstract:
To address the challenges posed by small detection targets, significant scale variations, and complex backgrounds in metal surface defect detection tasks, an improved model based on RT-DETR (real-time detection transformer) has been proposed. This model is referred to as HAS-DETR (high accuracy for small object-DETR). HAS-DETR enhances the feature extraction capability for small targets by introducing a multiple differential convolution module (MDConv) into the backbone network. A double multiscale feature fusion module is constructed to effectively capture global semantic information and detailed features, addressing the problem of scale variations. Additionally, a global multiscale attention mechanism has been developed to replace the multihead attention mechanism in the AIFI (attention-based intra-scale feature interaction) module. This modification has been shown to enhance the model’s robustness and accuracy in complex backgrounds and multiscale target scenarios. On the metal surface defect dataset, HAS-DETR has been demonstrated to achieve improvements of 6.5% in mAP50 and 4.5% in mAP50-95 compared to RT-DETR. On the public ADPPP dataset, the model demonstrates a 2.0% enhancement in mAP50 and a 1.3% improvement in mAP50-95. Experimental results demonstrate that HAS-DETR significantly enhances the detection accuracy for small objects in complex backgrounds while maintaining high detection efficiency. These findings indicate that HAS-DETR has strong potential for practical industrial applications.
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