In recent years, more and more scholars devoted themselves to the research of the target detection algorithm due to the continuous development of deep learning. Among them, the detection and recognition of small and complex targets are still a problem to be solved. The authors of this article have understood the shortcomings of the deep learning detection algorithm in detecting small and complex defect targets and would like to share a new improved target detection algorithm in steel surface defect detection. The steel surface defects will affect the quality of steel seriously. We find that most of the current detection algorithms for NEU-DET dataset detection accuracy are low, so we choose to verify a steel surface defect detection algorithm based on machine vision on this dataset for the problem of defect detection in steel production. A series of improvement measures are carried out in the traditional Faster R-CNN algorithm, such as reconstructing the network structure of Faster R-CNN. Based on the small features of the target, we train the network with multiscale fusion. For the complex features of the target, we replace part of the conventional convolution network with a deformable convolution network. The experimental results show that the deep learning network model trained by the proposed method has good detection performance, and the mean average precision is 0.752, which is 0.128 higher than the original algorithm. Among them, the average precision of crazing, inclusion, patches, pitted surface, rolled in scale and scratches is 0.501, 0.791, 0.792, 0.874, 0.649, and 0.905, respectively. The detection method is able to identify small target defects on the steel surface effectively, which can provide a reference for the automatic detection of steel defects.

中文翻译：

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基于深度学习的钢缺陷检测新算法

近年来，由于深度学习的不断发展，越来越多的学者致力于目标检测算法的研究。其中，对小的复杂目标的检测和识别仍然是一个需要解决的问题。本文的作者已经了解了深度学习检测算法在检测小而复杂的缺陷目标中的缺点，并希望分享一种在钢表面缺陷检测中新的改进的目标检测算法。钢的表面缺陷会严重影响钢的质量。我们发现，当前大多数用于NEU-DET数据集检测精度的检测算法均很低，因此我们选择基于机器视觉在该数据集上验证钢表面缺陷检测算法以解决钢铁生产中的缺陷检测问题。在传统的Faster R-CNN算法中进行了一系列改进措施，例如重建Faster R-CNN的网络结构。基于目标的小特征，我们使用多尺度融合来训练网络。对于目标的复杂特征，我们用可变形卷积网络代替了常规卷积网络的一部分。实验结果表明，该方法训练的深度学习网络模型具有良好的检测性能，平均平均精度为0.752，比原始算法高0.128。其中，开裂，夹杂物，斑块，点蚀表面，氧化皮和划痕的平均精度分别为0.501、0.791、0.792、0.874、0.649和0.905。