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Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning

机译：基于深度学习的汽车铸铝件X射线图像缺陷检测改进方法

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Nondestructive testing (NDT) for casting aluminum parts is an essential quality management procedure. In order to avoid the effects of human fatigue and improve detection accuracy, intelligent visual inspection systems are adopted on production lines. Conventional methods of defect detection can require heavy image pre-processing and feature extraction. This paper proposes a defect detection system based on X-ray oriented deep learning, which focuses on approaches that improve the detection accuracy at both the algorithm and data augmentation levels. Feature Pyramid Network (FPN) was primarily adopted for algorithm modification, which proved to be better suited for detecting small defects than Faster R-CNN, with a 40.9% improvement of the mean of Average Precision (mAP) value. In the final regression and classification stage, RoIAlign indicated apparent accuracy improvement in bounding boxes location compared with RoI pooling, which could increase accuracy by 23.6% under Faster R-CNN. Furthermore, different data augmentation methods compensated for the lack of datasets in X-ray image defect detection. Experiments found that an optimal mAP value existed, instead of it continuously increasing with the number of datasets rising for each data augmentation method. Research indicated that the three proposed improvement approaches performed better than baseline Faster R-CNN in X-ray image defect detection of automobile aluminum casting parts.
机译：铸造铝零件的无损检测（NDT）是必不可少的质量管理程序。为了避免人为疲劳的影响并提高检测精度，生产线上采用了智能的视觉检测系统。常规的缺陷检测方法可能需要大量的图像预处理和特征提取。本文提出了一种基于面向X射线的深度学习的缺陷检测系统，其重点是在算法和数据增强水平上均能提高检测精度的方法。特征金字塔网络（FPN）主要用于算法修改，事实证明，与快速R-CNN相比，特征金字塔网络更适合于检测小缺陷，平均精度（mAP）值提高了40.9％。在最后的回归和分类阶段，RoIAlign表示与RoI合并相比，边界框位置的精度明显提高，在Faster R-CNN下，精度可能提高23.6％。此外，不同的数据增强方法弥补了X射线图像缺陷检测中数据集的不足。实验发现，存在一种最佳的mAP值，而不是随着每种数据扩充方法的增加而随着数据集数量的增加而不断增加。研究表明，在汽车铝铸件的X射线图像缺陷检测中，提出的三种改进方法的性能优于基线Faster R-CNN。

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