Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model
This paper presents an enhanced Faster R-CNN model for detecting surface defects in resistance welding spots, improving both efficiency and accuracy for body-in-white quality monitoring. Key innovations include using high-confidence anchor boxes from the RPN network to locate welding spots, using th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/1/33 |
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| _version_ | 1832588062811488256 |
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| author | Weijie Liu Jie Hu Jin Qi |
| author_facet | Weijie Liu Jie Hu Jin Qi |
| author_sort | Weijie Liu |
| collection | DOAJ |
| description | This paper presents an enhanced Faster R-CNN model for detecting surface defects in resistance welding spots, improving both efficiency and accuracy for body-in-white quality monitoring. Key innovations include using high-confidence anchor boxes from the RPN network to locate welding spots, using the SmoothL1 loss function, and applying Fast R-CNN to classify detected defects. Additionally, a new pruning model is introduced, reducing unnecessary layers and parameters in the neural network, leading to faster processing times without sacrificing accuracy. Tests show that the model achieves over 90% accuracy and recall, processing each image in about 15 ms, meeting industrial requirements for welding spot inspection. |
| format | Article |
| id | doaj-art-687430c450d849629dba3dcd96e7b402 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-687430c450d849629dba3dcd96e7b4022025-01-24T13:39:12ZengMDPI AGMachines2075-17022025-01-011313310.3390/machines13010033Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN ModelWeijie Liu0Jie Hu1Jin Qi2School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, ChinaThis paper presents an enhanced Faster R-CNN model for detecting surface defects in resistance welding spots, improving both efficiency and accuracy for body-in-white quality monitoring. Key innovations include using high-confidence anchor boxes from the RPN network to locate welding spots, using the SmoothL1 loss function, and applying Fast R-CNN to classify detected defects. Additionally, a new pruning model is introduced, reducing unnecessary layers and parameters in the neural network, leading to faster processing times without sacrificing accuracy. Tests show that the model achieves over 90% accuracy and recall, processing each image in about 15 ms, meeting industrial requirements for welding spot inspection.https://www.mdpi.com/2075-1702/13/1/33resistance spot weldingsurface defect detectiondeep learning modelfaster R-CNNsmall object detection |
| spellingShingle | Weijie Liu Jie Hu Jin Qi Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model Machines resistance spot welding surface defect detection deep learning model faster R-CNN small object detection |
| title | Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model |
| title_full | Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model |
| title_fullStr | Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model |
| title_full_unstemmed | Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model |
| title_short | Resistance Spot Welding Defect Detection Based on Visual Inspection: Improved Faster R-CNN Model |
| title_sort | resistance spot welding defect detection based on visual inspection improved faster r cnn model |
| topic | resistance spot welding surface defect detection deep learning model faster R-CNN small object detection |
| url | https://www.mdpi.com/2075-1702/13/1/33 |
| work_keys_str_mv | AT weijieliu resistancespotweldingdefectdetectionbasedonvisualinspectionimprovedfasterrcnnmodel AT jiehu resistancespotweldingdefectdetectionbasedonvisualinspectionimprovedfasterrcnnmodel AT jinqi resistancespotweldingdefectdetectionbasedonvisualinspectionimprovedfasterrcnnmodel |