6 Conclusion
⑴ In this paper, the Faster RCNN
model and the RetinaNet model are studied. The surface defects of
Si3N4 ceramic bearing inner ring are
taken as the research object. The backbone of the RetinaNet model is
ResNet-50. The performance of RetinaNet model is improved by comparing
Faster RCNN with RetinaNet. SE,
CBAM and NAM attention modules are added to the tail of ResNet to form
attention modules. Comparing the effects of different attentional
modules on model performance, NAM attentional module has the most
significant effect on model performance. NAM attentional module is used
to form improved RetinaNet model.
⑵ The surface defects images are collected by the platform established
by ourselves and made into dataset by LabelIMG. The online data
augmentation is used to expand the surface defect dataset of
Si3N4 ceramic bearing inner ring. The
improved Faster RCNN model and RetinaNet model are used to train the
surface defect dataset and verify the model performance. This improved
RetinaNet has the characteristics of high precision in pit, wear, crack
and snowflake detection, and is superior to Faster RCNN model. Compared
with the Faster RCNN model and the RetinaNet model, the mAP value of the
improved RetinaNet model increased by 13.45% and 2.1%, respectively.