An improved Faster R-CNN network for aeroengine fuse fracture detection

Liao Minjie; Bo Lin; Wu Xialing; Liu Qunyang; Wu Wenhong

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An improved Faster R-CNN network for aeroengine fuse fracture detection

Liao Minjie 1,Bo Lin 2,Wu Xialing 2,Liu Qunyang 2,Wu Wenhong 2 *

1.China aeroengine South industry Co., Ltd., Zhuzhou 412002;China aeroengine South industry Co., Ltd., Zhuzhou 412002;China aeroengine South industry Co., Ltd., Zhuzhou 412002;Zhuzhou Xiangyun Zhihang Technology Development Co., Ltd., Zhuzhou 412002;Zhuzhou Xiangyun Zhihang Technology Development Co., Ltd., Zhuzhou 412002

*Correspondence author

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Funding: none

Opened online:18 December 2020

Accepted by: none

Citation: Liao Minjie,Bo Lin,Wu Xialing.An improved Faster R-CNN network for aeroengine fuse fracture detection[OL]. [18 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753217

In order to meet the needs of aeroengine fuse fracture detection in practical application, an improved Faster R-CNN small target detection network is proposed. Firstly, FPN feature graph pyramid is added to improve the extraction ability of small target features, and then ROI Align is used to replace ROI pooling to reduce the loss of feature information of small targets. Experiments on the fuse fracture data set show that the improved detection network is 5.76% higher than Faster R-CNN on mAP. The experimental results show that the improved network is more advanced and has a practical application prospect in aeroengine fuse fracture detection based on computer vision.

Keywords:Image recognition; Faster R-CNN; Small target detection

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