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• W4315705099 abstract "The visual automatic detection method based on artificial intelligence has attracted more and more attention. In order to improve the performance of weld nondestructive defect detection, we propose DRepDet (Dilated RepPoints Detector). First, we analyze the weld defect dataset in detail and summarize the distribution characteristics of weld defect data, that is, the defect scale is very different and the aspect ratio distribution range is large. Second, according to the distribution characteristics of defect data, we design DResBlock module, and introduce dilated convolution with different dilated rates in the process of feature extraction to expand the receptive field and improve the detection performance of large-scale defects. Based on DResBlock and anchor-free detection framework RepPoints, we design DRepDet. Extensive experiments show that our proposed detector can detect 7 types of defects. When using combined dilated rate convolution network in detection, the AP50 and Recall50 of big defects are improved by 3.1% and 3.3% respectively, while the performance of small defects is not affected, almost the same or slightly improved. The final performance of the whole network is improved a large margin, with 6% AP50 and 4.2% Recall50 compared with Cascade R-CNN and 1.4% AP50 and 2.9% Recall50 compared with RepPoints." @default.
• W4315705099 created "2023-01-12" @default.
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• W4315705099 date "2022-12-01" @default.
• W4315705099 modified "2023-10-01" @default.
• W4315705099 title "Automatic Detection of Weld Defects in Pressure Vessel X-Ray Image Based on CNN" @default.
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• W4315705099 doi "https://doi.org/10.1051/wujns/2022276489" @default.
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