A damage detection system for inner bore of electromagnetic railgun launcher based on deep learning and computer vision

作者：

Highlights：

• An automated system for railgun launcher damage detection is proposed.

• A new data set of railgun inner bore damage is obtained by the designed device.

• Adaptive data augmentation and focal loss are used to balance the uneven data set.

• YOLOv5 and SOLOv2 are used for the detection and shape extraction of damage.

• Results on the data set show the effectiveness of the proposed methods.

摘要

•An automated system for railgun launcher damage detection is proposed.•A new data set of railgun inner bore damage is obtained by the designed device.•Adaptive data augmentation and focal loss are used to balance the uneven data set.•YOLOv5 and SOLOv2 are used for the detection and shape extraction of damage.•Results on the data set show the effectiveness of the proposed methods.

论文关键词：Railgun,Damage detection,Artificial neural networks,Object detection,Instance segmentation,Data augmentation

论文评审过程：Received 21 June 2021, Revised 9 December 2021, Accepted 25 April 2022, Available online 2 May 2022, Version of Record 5 May 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117351