改进YOLOv7的航空发动机叶片损伤检测方法

Improved YOLOv7 Damage Detection Method for Aeroengine Blade

中文关键词:  损伤检测  深度学习  YOLOv7模型  注意力机制  航空发动机

英文关键词:damage detection  deep learning  YOLOv7 model  attention mechanism  aeroengine

中文摘要:

      针对目前航空发动机叶片损伤检测精度低的问题，提出了一种基于改进YOLOv7的发动机叶片损伤检测模型YOLOv7- CC。对发动机叶片缺损图像进行损伤标注，构建航空发动机叶片损伤数据集，并且采用二分K-means算法对标记框进行聚类，获 取与该数据集最匹配的锚框（anchor）。在模型中Backbone网络输出之后采用坐标注意力机制，分别捕获长距离依赖关系和保留 精确的位置信息，提高对损伤目标的检测能力，并在特征重组过程中采用轻量级上采样算子（CARAFE），同时保留了语义信息以 及位置信息，通过更大的感受野来完成上采样，提高了网络对特征的提取能力。结果表明：所提出的基于YOLOv7-CC算法的损伤 检测的平均精度达到了83.53%，相较于基准网络提升了7.4%，能够对航空发动机叶片3种常见的损伤类型实现高效检测。

英文摘要:

      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.