  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.
