Cracks on sheet metals can significantly affect the overall strength. Crack detection during manufacturing is, thus, an important process for the quality assessment on a press line. Deep learning, a data-driven structure, has been extensively used to detect cracks on various surfaces. In this study, a crack detection technique for a press line using Retina Net and a novel data augmentation method is proposed, which mainly focuses on three steps, shape acquisition, style transfer, and edge fusion. First, the shapes of crack on different materials are extracted. Then, images are created by providing metal crack textures to those shapes using a fusion network with a relatively small number of real crack images. Real crack images are captured from a sheet metal forming line. Training data can be enriched using the proposed data augmentation method. Validation experiments are conducted to demonstrate the effectiveness of the proposed crack detection and data augmentation techniques.
The computer vision-based measurements offer the superior capabilities over traditional sensing systems, including high spatial resolutions, no mass-loading effects, and low cost. This capability allows the vision-based technique to be widely applied to the damage detection practice as a more efficient way compared to conventional methods. The recently developed phase-based motion processing technique can measure the displacement signals with high accuracy and noise robustness. In this study, an automated damage identification and localization technique based on the phase-based motion processing is proposed. The local phase of the object is extracted using a optimal steerable filter. The modal parameters are then obtained after performing the morphological operation on the edge image in conjunction with the phase signals. Damage is finally localized and assessed by the features extracted by the identified modal parameters. Several experiments are carried out to validate the proposed technique on a 5-story structure under different bolt losing conditions. The experimental results show that the proposed technique can automatically detect and evaluate the damage with high accuracy.
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