Research on small target damage detection of aero-engine based on improved YOLOv4
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摘要:
智能化的航空发动机损伤检测是飞机故障诊断重要的研究方向，针对现有目标检测模型对航空发动机的小目标损伤检测效果差的问题，提出了一种改进的基于You Only Look Once version 4（YOLOv4）的多尺度目标检测方法。在路径聚合网络（PANet）中构建低层次的特征融合层，将更浅层的特征与深层特征融合，提高网络对小目标损伤的检测性能。为减少网络中的冗余参数，在颈部结构中引入了深度可分离卷积，将标准卷积重构为深度可分离卷积的形式。实验表明：改进后的YOLOv4对小目标损伤的检测精度提升了3.43%，模型大小降低了54.06 MB，同时检测速度提高了31.03%。研究结果表明改进的YOLOv4模型对小目标损伤具有更好的检测性能。
Abstract:Intelligent aero-engines damage detection is an important research direction in aircraft fault diagnosis. An improved multi-scale target detection method based on You Only Look Once version 4 (YOLOv4) was proposed for the problem that existing target detection model has a poor effect on the detection of small target damage of aero-engine. A new shallow feature fusion layer was constructed in path aggregation network （PANet）, which fused shallower features with deep features to improve the network detection performance for small target damage. In order to reduce redundant parameters in the network, depthwise separable convolution was introduced in neck and the standard convolution was reconstructed into the form of depthwise separable convolution. Experiments showed that the improved YOLOv4 improved the detection accuracy of small target damage by 3.43%, reduced the model size by 54.06 MB, and increased the detection speed of the model by 31.03%. The results of the study indicated that the improved YOLOv4 model had better detection performance for small target damage.
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图 4 标准卷积和深度可分离卷积过程
Figure 4. Process of standard convolution and depthwise separable convolution
表 1 消融实验结果
Table 1. Ablation experiment results
方法 P/% 模型
大小/MB参数量 检测速度/
（帧/s）YOLOv4-a 92.74 244.25 64029231 29 YOLOv4-b 96.90 248.34 65100220 27 YOLOv4-c 96.17 190.19 49856380 38 
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表 2 不同模型检测性能对比分析
Table 2. Comparative analysis of detection performance of different models
模型 P/% 模型
大小/MB参数量 检测速度/
（帧/s）SSD 85.28 90.07 23612246 43 YOLOv3 90.50 234.69 61523734 31 Faster R-CNN 94.83 521.43 136689024 11 YOLOv4-c 96.17 190.19 49856380 38
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