A damage detection system for inner bore of electromagnetic railgun launcher based on deep learning and computer vision. (15th September 2022)

• Record Type: Journal Article
• Authors: Zhou, Yu; Cao, Ronggang; Li, Ping; Ma, Xiao; Hu, Xueyi; Li, Fadong
• Is Part Of: Expert systems with applications. Volume 202(2022)
• Publication Date: 2022-09-15
• Subjects: Railgun -- Damage detection -- Artificial neural networks -- Object detection -- Instance segmentation -- Data augmentation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117351
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Abstract: The inner bore damage affects the launch performance and service life of electromagnetic railgun launcher. Detection and observation of railgun inner bore damage contribute to the study on mechanism and development rules of railgun damage. This paper analyzes five types of typical railgun inner bore damage. Based on the detection requirement for the damages, this paper proposes an automated damage detection system for the inner bore of electromagnetic railgun launcher consisting of data acquisition device and detection algorithms. The proposed device can step inside the inner bore of the railgun launcher to take photos of the inner bore surface automatically. We use the images obtained by the proposed device to build a data set for the training and verification of the detection algorithms. The object detection algorithm You Only Look Once v5 (YOLOv5) is utilized to achieve the rapid detection of railgun inner bore damages. We introduce the adaptive data augmentation and the focal loss to balance out the uneven category distribution of our data set. The result proves that our YOLOv5 model reaches the state-of-the-art level in the railgun inner bore damage detection task, with its mean Average Precision (mAP) of 0.659 and detection speed of 47.6 frames per second (fps). We choose Segmenting Objects by Locations v2 (SOLOv2) to extract the shape of the damage, with the Average Precision of 0.631. We further achieve damage statistics and the model visualization of damage distribution. The experimental results show that the proposed detection system meets the requirements of rapid detection and accurate feature extraction. It provides researchers with an approach for the study of railgun inner bore damage mechanism.