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首页> 外文期刊>ISIJ international >Enhanced Faster Region Convolutional Neural Networks for Steel Surface Defect Detection
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Enhanced Faster Region Convolutional Neural Networks for Steel Surface Defect Detection

机译：增强的快速区域卷积神经网络用于钢表面缺陷检测

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摘要

Bar steel surface defects detection is very important to steel production and quality control. Many traditional computer vision methods have been applied to industrial defects detection, but they are usually environmentally sensitive and not robust enough. In this paper, a deep learning defects detection method based on Faster Region Convolutional Neural Networks (Faster R-CNN) is proposed. Firstly, to solve the problem of missed detection of a large number of small defects, we introduce Weighted Region of Interest (RoI) Pooling instead of RoI pooling, which eliminates the area misalignment caused by the two quantization processes in the latter, and the small defects detection rate is significantly improved. Secondly, considering that most of the defects are irregular in shape, we use deformable convolution in upper layers to adapt to various shapes by learning the positional offset in convolution. Thirdly, owing to the diversity of bar steel defects, multi-scale feature extraction network with Feature Pyramid Networks (FPN) is proposed to build feature pyramids. Finally, we propose Strict-Non-Maximum Suppression (Strict-NMS) algorithm to reduce overlapping bounding boxes as much as possible. Experiments on defect datasets in real industrial environments show that the detection rate of this method can reach 97%, which is much higher than state-of-the-art methods.
机译：钢筋表面缺陷的检测对钢铁生产和质量控制非常重要。许多传统的计算机视觉方法已经应用于工业缺陷检测，但是它们通常对环境敏感并且不够坚固。提出了一种基于快速区域卷积神经网络（Faster R-CNN）的深度学习缺陷检测方法。首先，为了解决漏检大量小缺陷的问题，我们引入了加权感兴趣区域（RoI）池代替RoI池，从而消除了后者中两个量化过程所导致的面积失配，并且缺陷检测率大大提高。其次，考虑到大多数缺陷是不规则形状的，我们通过学习卷积中的位置偏移量，在上层使用可变形的卷积来适应各种形状。第三，由于钢筋缺陷的多样性，提出了使用特征金字塔网络（FPN）的多尺度特征提取网络来构建特征金字塔。最后，我们提出严格非最大抑制（Strict-NMS）算法，以尽可能减少重叠的边界框。在实际工业环境中对缺陷数据集进行的实验表明，该方法的检测率可以达到97％，远高于最新方法。

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