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TruingDet: Towards high-quality visual automatic defect inspection for mental surface

机译：TRUINGDET：对精神表面的高质量视觉自动缺陷检查

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摘要

Visual surface defect detection, which aims to obtain the locations of defects and classify each defect into the corresponding category in a given image, is a critical task in an actual production process. Nowadays, more and more methods have made excellent progress in visual defect inspection. However, there still exist three tough challenges where these methods cannot handle well: large defect shape change, large-scale variation, and high-quality defect localization. In this paper, a Convolutional Neural Networks (CNN) based visual defect detection framework is proposed, which elegantly mitigated these three problems by introducing three well-designed components including deformable convolution module, balanced feature pyramid module and cascade head module. First, the feature maps contained with defect shape information are adaptively extracted by Resnet/ResneXt network with the deformable convolution operator. Then the balanced feature pyramid module is attached to the feature extraction module to obtain information-fused multilayer feature maps. Finally, the cascade head is applied to refine the predicted bounding box to achieve high-quality defect localization. Under the COCO evaluation metrics, our method significantly obtains 45.2 mAP with a large margin (4.9 AP) compared with Faster RCNN baseline.
机译：视觉表面缺陷检测，旨在获得缺陷的位置并将每个缺陷分类为给定图像中的相应类别，是实际生产过程中的关键任务。如今，越来越多的方法在视觉缺陷检查方面取得了良好的进展。但是，这些方法无法处理的三种艰难的挑战：大的缺陷形状变化，大规模变化和高质量的缺陷定位。本文提出了一种基于卷积神经网络（CNN）的视觉缺陷检测框架，通过引入包括可变形卷积模块，平衡特征金字塔模块和级联头模块，通过引入三种精心设计的部件，典雅地减轻了这三个问题。首先，用可变形卷积操作员通过Reset / Resnext网络自适应地提取缺陷形状信息的特征映射。然后，平衡功能金字塔模块附加到特征提取模块，以获取信息融合的多层特征映射。最后，级联头应用于改进预测的边界盒以实现高质量的缺陷本地化。在Coco评估指标下，我们的方法显着获得了45.2个映射，与速度的RCNN基线相比，具有大边距（4.9 AP）。

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