Recognition and classification for surface defects of Si 3 N 4 ceramic
bearing inner ring based on RetinaNet method and NAM attention mechanism
Abstract
Due to surface defects on inner ring of Si 3N
4 ceramic bearing are tiny and difficult to defect, the
defects accelerate the wear of ceramic parts and reduce the performance
of ceramic parts. A surface defect detection method based on RetinaNet
method and NAM attention mechanism is proposed. Besides, the performance
of RetinaNet method and Faster RCNN method is compared. The platform for
surface defects of Si 3N 4 ceramic
bearing inner ring is built independently to collect images. The dataset
is made up of collected images and expanded by online data augmentation.
Resnet-50 is used as the feature extraction network. The NAM attention
mechanism is added to the tail of Resnet-50 to form an attention module
to improve the model accuracy. As the bounding box regression loss
function, loss is used for learning bounding box regression and
localization uncertainty. A multi-scale feature pyramid is constructed
by a feature pyramid network to integrate multi-level feature
information. And a small full convolutional network is used as a
classification sub-network and a bounding box regression sub-network.
The results show that the mAP of the method reaches 91.84%, which is
13.45% and 2.1% higher compared to Faster RCNN and RetinaNet,
respectively. The method has good detection effect on the identification
and classification of surface defect species.