Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR
Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detect...
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| Veröffentlicht in: | Scientific reports Jg. 15; H. 1; S. 29720 - 21 |
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13.08.2025
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| Abstract | Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. To address this trade-off, this study introduces an improved detection method based on the Real-Time DEtection TRansformer(RT-DETR) framework. First, a large-kernel attention mechanism is integrated into the backbone to enhance multi-scale feature extraction and fusion, while reducing architectural redundancy. Second, the RepC3 structure within the cross-scale fusion module is replaced with a module based on the generalized-efficient layer aggregation network that uses a more efficient layer aggregation strategy to improve feature localization. Finally, the Adown downsampling module is introduced, employing a multi-path design that reduces parameter count while preserving critical feature details during scale reduction. Experimental results on our industrial filter surface defect dataset show that the enhanced RT-DETR model achieves a mean average precision of 97.6%, a 7.3 percentage point increase over the baseline. Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. Generalization experiments on the public NEU-DET dataset and GC10-DET dataset further confirm the model’s robustness and effectiveness, demonstrating its suitability for industrial applications requiring both high accuracy and lightweight deployment. |
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| AbstractList | Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. To address this trade-off, this study introduces an improved detection method based on the Real-Time DEtection TRansformer(RT-DETR) framework. First, a large-kernel attention mechanism is integrated into the backbone to enhance multi-scale feature extraction and fusion, while reducing architectural redundancy. Second, the RepC3 structure within the cross-scale fusion module is replaced with a module based on the generalized-efficient layer aggregation network that uses a more efficient layer aggregation strategy to improve feature localization. Finally, the Adown downsampling module is introduced, employing a multi-path design that reduces parameter count while preserving critical feature details during scale reduction. Experimental results on our industrial filter surface defect dataset show that the enhanced RT-DETR model achieves a mean average precision of 97.6%, a 7.3 percentage point increase over the baseline. Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. Generalization experiments on the public NEU-DET dataset and GC10-DET dataset further confirm the model's robustness and effectiveness, demonstrating its suitability for industrial applications requiring both high accuracy and lightweight deployment.Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. To address this trade-off, this study introduces an improved detection method based on the Real-Time DEtection TRansformer(RT-DETR) framework. First, a large-kernel attention mechanism is integrated into the backbone to enhance multi-scale feature extraction and fusion, while reducing architectural redundancy. Second, the RepC3 structure within the cross-scale fusion module is replaced with a module based on the generalized-efficient layer aggregation network that uses a more efficient layer aggregation strategy to improve feature localization. Finally, the Adown downsampling module is introduced, employing a multi-path design that reduces parameter count while preserving critical feature details during scale reduction. Experimental results on our industrial filter surface defect dataset show that the enhanced RT-DETR model achieves a mean average precision of 97.6%, a 7.3 percentage point increase over the baseline. Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. Generalization experiments on the public NEU-DET dataset and GC10-DET dataset further confirm the model's robustness and effectiveness, demonstrating its suitability for industrial applications requiring both high accuracy and lightweight deployment. Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. To address this trade-off, this study introduces an improved detection method based on the Real-Time DEtection TRansformer(RT-DETR) framework. First, a large-kernel attention mechanism is integrated into the backbone to enhance multi-scale feature extraction and fusion, while reducing architectural redundancy. Second, the RepC3 structure within the cross-scale fusion module is replaced with a module based on the generalized-efficient layer aggregation network that uses a more efficient layer aggregation strategy to improve feature localization. Finally, the Adown downsampling module is introduced, employing a multi-path design that reduces parameter count while preserving critical feature details during scale reduction. Experimental results on our industrial filter surface defect dataset show that the enhanced RT-DETR model achieves a mean average precision of 97.6%, a 7.3 percentage point increase over the baseline. Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. Generalization experiments on the public NEU-DET dataset and GC10-DET dataset further confirm the model’s robustness and effectiveness, demonstrating its suitability for industrial applications requiring both high accuracy and lightweight deployment. Abstract Filters are critical components in automotive engine systems, responsible for maintaining stable operation by removing impurities from liquids and gases. Their performance is highly sensitive to surface defects, rendering high-precision automated inspection essential. However, existing defect detection algorithms often struggle to balance between detection accuracy and the computational efficiency required for industrial deployment. To address this trade-off, this study introduces an improved detection method based on the Real-Time DEtection TRansformer(RT-DETR) framework. First, a large-kernel attention mechanism is integrated into the backbone to enhance multi-scale feature extraction and fusion, while reducing architectural redundancy. Second, the RepC3 structure within the cross-scale fusion module is replaced with a module based on the generalized-efficient layer aggregation network that uses a more efficient layer aggregation strategy to improve feature localization. Finally, the Adown downsampling module is introduced, employing a multi-path design that reduces parameter count while preserving critical feature details during scale reduction. Experimental results on our industrial filter surface defect dataset show that the enhanced RT-DETR model achieves a mean average precision of 97.6%, a 7.3 percentage point increase over the baseline. Furthermore, the model reduces parameter count by 6.9% and computational load by 13.1%, demonstrating its improved efficiency. Generalization experiments on the public NEU-DET dataset and GC10-DET dataset further confirm the model’s robustness and effectiveness, demonstrating its suitability for industrial applications requiring both high accuracy and lightweight deployment. |
| ArticleNumber | 29720 |
| Author | Zhao, Chunxia Liu, Yingxiao Bao, Zhikang Wei, Xiaojuan Chen, Mengxu Zhang, Maoyuan Liu, Guojun Guo, Yunfeng An, Run Zhao, Pengcheng |
| Author_xml | – sequence: 1 givenname: Maoyuan surname: Zhang fullname: Zhang, Maoyuan organization: College of Electrical Engineering, Northwest Minzu University, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking – sequence: 2 givenname: Xiaojuan surname: Wei fullname: Wei, Xiaojuan email: weixiaojuan925@126.com organization: College of Electrical Engineering, Northwest Minzu University, Zhejiang Zhenhang Industrial Group Company Ltd., College of Electrical and Information Engineering, Lanzhou University of Technology, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking – sequence: 3 givenname: Guojun surname: Liu fullname: Liu, Guojun organization: Zhejiang Zhenhang Industrial Group Company Ltd – sequence: 4 givenname: Mengxu surname: Chen fullname: Chen, Mengxu organization: Zhejiang Zhenhang Industrial Group Company Ltd – sequence: 5 givenname: Chunxia surname: Zhao fullname: Zhao, Chunxia organization: College of Electrical Engineering, Northwest Minzu University, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking – sequence: 6 givenname: Yingxiao surname: Liu fullname: Liu, Yingxiao organization: College of Electrical Engineering, Northwest Minzu University, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking – sequence: 7 givenname: Zhikang surname: Bao fullname: Bao, Zhikang organization: College of Electrical Engineering, Northwest Minzu University, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking, Yunnan power grid limited liability company Wenshan Power Supply Bureau – sequence: 8 givenname: Yunfeng surname: Guo fullname: Guo, Yunfeng organization: College of Electrical Engineering, Northwest Minzu University, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking – sequence: 9 givenname: Run surname: An fullname: An, Run organization: College of Electrical Engineering, Northwest Minzu University, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking – sequence: 10 givenname: Pengcheng surname: Zhao fullname: Zhao, Pengcheng organization: College of Electrical Engineering, Northwest Minzu University, Gansu Engineering Research Center for Eco-Environmental Intelligent Networking |
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| SubjectTerms | 639/705/117 639/705/258 Accuracy Classification Computer applications Deep learning Defects Efficiency Filters Humanities and Social Sciences Impurities Industrial applications Localization multidisciplinary Network management systems Queries Real time Science Science (multidisciplinary) Semantics |
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| Title | Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR |
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