Research on Weld Defect Detection Method Based on Improved DETR

DAI Zheng; LIU Xiaojia; PAN Quan

doi:10.11999/JEIT241009

Volume 47 Issue 7

Jul. 2025

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DAI Zheng, LIU Xiaojia, PAN Quan. Research on Weld Defect Detection Method Based on Improved DETR[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2298-2307. doi: 10.11999/JEIT241009

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DAI Zheng, LIU Xiaojia, PAN Quan. Research on Weld Defect Detection Method Based on Improved DETR[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2298-2307. doi: 10.11999/JEIT241009

DAI Zheng, LIU Xiaojia, PAN Quan. Research on Weld Defect Detection Method Based on Improved DETR[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2298-2307. doi: 10.11999/JEIT241009

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Research on Weld Defect Detection Method Based on Improved DETR

doi: 10.11999/JEIT241009 cstr: 32379.14.JEIT241009

DAI Zheng^{1, 2},
LIU Xiaojia²,
PAN Quan^{1
,
,}

1.
Northwestern Polytechnical University, Xi’an 710072, China
2.
Shanghai Spaceflight Precision Machinery Institute, Shanghai 201600, China

Funds: Shanghai Pujiang Program (20PJ1405000)

Received Date: 2024-11-12
Rev Recd Date: 2025-04-10

Available Online: 2025-04-25

Publish Date: 2025-07-22

Abstract

Abstract

Objective Welding technology plays a pivotal role in industrial manufacturing, where X-ray image evaluation serves as a critical inspection method for assessing the internal quality of weld seams. X-ray inspection is effective in identifying defects such as slag inclusions, incomplete penetration, and porosity, which helps prevent structural failures and ensures the reliability and durability of welded components. This process is a fundamental quality control measure in industrial manufacturing. However, challenges persist in the assessment of weld seam X-ray images, particularly in relation to high workloads and inefficiencies. Conventional models often experience multi-scale feature information loss during feature extraction due to the significant variation in the size and morphology of defects, such as porosity, slag inclusions, and incomplete penetration, found in large structural weld seams. To address these limitations, the Detection Transformer with Concatenated Expand Convolutions and Augmented Feature Pyramid Networks (CADETR) model is proposed to improve detection performance for weld defects in large structural components. Methods The CADETR model is proposed for detecting weld defects in large structural components. The model comprises three core components: the DETR network, concatenated expand convolution (CEC) network, and Augmented Feature Pyramid Network (AFPN). The DETR network applies multi-head self-attention mechanisms to effectively capture global contextual relationships among feature map positions, enhancing perceptual capability and detection accuracy for weld defects. The CEC module adopts a composite expanded convolution structure, widening convolutional kernel receptive fields and significantly improving feature extraction for defects across various scales. The AFPN module reinforces multi-scale defect feature extraction by integrating hierarchical feature maps and employing a feature batch elimination mechanism, reducing overfitting and enhancing generalization performance in multi-scale defect detection. Additionally, a Penalized Cross Entropy Loss (PCE-Loss) function is proposed, which applies increased penalties to incorrect defect predictions, further improving model robustness and precision. Results and Discussions The performance of the CADETR defect detection model is evaluated through a comparative analysis with multiple models, including Faster RCNN, ECASNet, GeRCNN, DETR, MDCBNet, HPRT-DETR, and YOLOV11. Weld seam X-ray image data are input into each model, with variations in loss values recorded during the training process. Model performance in defect detection is assessed using Precision, Recall, and mAP metrics. Experimental results show that the CADETR model exhibits slightly higher loss values compared to HPRT-DETR and YOLOV11 but lower than other benchmark models (Fig. 7). The CADETR model demonstrates superior performance in mAP, achieving 91.6%, exceeding all comparative models (Table 3). The CADETR model proves particularly effective in detecting defects characterized by a high proportion of small targets and significant shape variations (Fig. 8). Conclusions This study addresses the challenges of detecting weld defects with significant variations in size and morphology in large structural components through the CADETR weld defect detection model. Evaluation using a welded seam X-ray image dataset revealed the following key findings: (1) The sequential integration of the CEC module, AFPN module, and PCE-Loss function into the baseline DETR framework improved mAP by 4.6%, 4.5%, and 3.4%, respectively, validating the contribution of each component. (2) The CADETR model achieved a 91.6% mAP for weld defect detection, with a single-image inference time of 0.036 s. (3) Compared to the original DETR, CADETR demonstrated a 8.9% improvement in mAP. For future implementation, the CADETR model will be deployed in a Browser/Server (B/S) architecture-based weld defect detection system, where both software algorithms and computational hardware resources will be hosted on cloud servers. This design ensures stable operational workflows and facilitates cross-platform data resource sharing.
- Weld Seam,
- Defect detection,
- Multi-scale,
- mean Average Precision(mAP)

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References(22)

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