论文标题
使用神经网络对铸件缺陷的自动缺陷识别
Automated Defect Recognition of Castings defects using Neural Networks
论文作者
论文摘要
工业X射线分析在需要保证某些零件的结构完整性的航空航天，汽车或核行业中很常见。但是，射线照相图像的解释有时很困难，可能导致两名专家在缺陷分类上不同意。本文介绍的自动缺陷识别（ADR）系统将减少分析时间，还将有助于减少对缺陷的主观解释，同时提高人类检查员的可靠性。我们的卷积神经网络（CNN）模型达到了94.2 \％的准确性（MAP@iou = 50 \％），当应用于该数据集的ART现状时，它被认为与预期的人类性能相似。在工业环境上，其推理时间少于每个DICOM图像，因此可以安装在生产设施上而不会影响交货时间。此外，还进行了对主要高参数的消融研究，以优化模型准确性，从75 \％映射的初始基线结果最高为94.2 \％地图。
Industrial X-ray analysis is common in aerospace, automotive or nuclear industries where structural integrity of some parts needs to be guaranteed. However, the interpretation of radiographic images is sometimes difficult and may lead to two experts disagree on defect classification. The Automated Defect Recognition (ADR) system presented herein will reduce the analysis time and will also help reducing the subjective interpretation of the defects while increasing the reliability of the human inspector. Our Convolutional Neural Network (CNN) model achieves 94.2\% accuracy (mAP@IoU=50\%), which is considered as similar to expected human performance, when applied to an automotive aluminium castings dataset (GDXray), exceeding current state of the art for this dataset. On an industrial environment, its inference time is less than 400 ms per DICOM image, so it can be installed on production facilities with no impact on delivery time. In addition, an ablation study of the main hyper-parameters to optimise model accuracy from the initial baseline result of 75\% mAP up to 94.2\% mAP, was also conducted.
