| Abstract:Aiming at the current problem of low accuracy of aeroengine blade damage detection, an engine blade damage detection
model YOLOv7-CC based on improved YOLOv7 was proposed. The engine blade defect images were labeled with damage to construct an
aeroengine blade damage dataset, and the labeled frames were clustered using the bifurcated K-means algorithm to obtain the anchors that
best match this dataset. After the output of Backbone network in the model, the coordinate attention mechanism was used to capture the
long-distance dependency and retain the accurate position information respectively, to improve the detection ability of the damage target,
and the CARAFE lightweight up-sampling algorithm was used during the feature reorganization process, retaining the semantic information
as well as the positional information at the same time; the up-sampling was completed through the larger sensory field, improving the
feature extraction ability of the network. The results show that the proposed YOLOv7-CC algorithm for damage detection achieves an aver?
age accuracy of 83.53%, which is a 7.4% improvement compared to the baseline network, and is able to realize highly efficient detection of
the three common damage types of aeroengine blades. |
