Defect Detection of Printed Fabric Based on RGBAAM and Image Pyramid

Jing, Junfeng; Ren, Huanhuan

doi:10.2478/aut-2020-0007

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…This method can effectively segment printed fabric defects with low calculation cost, but the detection speed is relatively slow. 31 Liu et al 4 proposed a weakly supervised shallow network, which integrates Link-SE (L-SE) module and Dilation Up-Weight CAM (DUW-CAM), fabric background features are suppressed through DUW-CAM with attention mechanism, and defect areas are located more accurately, but the calculation process is cumbersome.…”

Section: Introductionmentioning

confidence: 99%

Fabric defect detection based on transfer learning and improved Faster R-CNN

Zhao

Shi

Zheng

et al. 2022

Journal of Engineered Fibers and Fabrics

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Fabric defect detection is an important quality inspection process in the textile industry. A fabric defect detection system based on transfer learning and an improved Faster R-CNN is proposed to solve the problems of low detection accuracy, general convergence ability, and poor detection effect for small target defects in existing fabric defect detection algorithms. The pre-trained weights on the big dataset Imagenet are first extracted for transfer learning. Images are then input into the improved Faster R-CNN network, while the ResNet50 and ROI Align are used to replace the original VGG16 feature extraction network structure and a region of interest (ROI) pooling layer to avoid the problems of region mismatch caused by two quantizations from ROI pooling. The region proposal network (RPN) is combined with the multi-scale feature pyramid FPN to generate candidate regions with richer semantic information and project them onto the feature map to obtain the corresponding feature matrix. Cascaded modules are integrated and different IoU thresholds are used for each level to distinguish positive and negative samples. Finally, the softmax classifier is adopted to identify the image and obtain the predictions. The experimental results show that the detection accuracy and convergence ability of the improved Faster R-CNN are greatly enhanced compared with the current mainstream models, which provides a reference for future fabric defect detection methods.

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Section: Introductionmentioning

confidence: 99%

Fabric defect detection based on transfer learning and improved Faster R-CNN

Zhao

Shi

Zheng

et al. 2022

Journal of Engineered Fibers and Fabrics

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“…(17) In combination with the features of a multilevel image pyramid, (18) the effect of noise caused by a scale change is reduced and the detection effect is enhanced. (19,20) Toward solving the above-mentioned multiscale crater detection problem, we propose a pyramidal image segmentation method based on U-Net to automate multiscale crater extraction. This method first builds an image pyramid and integrates the craters of each layer of the image pyramid extracted by U-Net to reduce the impact of the scale on the extraction of craters; the conversion relationship between the segmented images of the multilayer image pyramid and the geographic coordinates of the craters is established to solve the problem of non-offset splicing of craters in large areas.…”

Section: Introductionmentioning

confidence: 99%

Pyramidal Image Segmentation Based on U-Net for Automatic Multiscale Crater Extraction

Hong¹,

Zhai²,

Zhou³

et al. 2022

Sensors and Materials

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To extract craters with a radius greater than 10 km more effectively from lunar digital elevation maps, pyramidal image segmentation based on the U-Net model is proposed, and the conversion relationship between the multilayer image pyramid and the geographic coordinates of the crater is established. The crater image pyramid method ensures the full coverage of the study area with a small number of images and that each crater exists in several images with different resolutions. The proposed method can effectively improve the detection performance of large-scale craters and solve the migration problem when stitching together craters from largescale images. This method recovered 85.48% of the craters with a radius greater than 10 km in an artificially annotated dataset, found 1044 new craters, and extended the maximum radius of detected craters from 72 km in randomly cropped image segmentation to 200 km. It was estimated by visual interpretation that approximately 82.09% of these new craters are real. Also, the recall reaches 90.17% when the new real craters are added to the true craters.

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