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

Zhou, Yu; Cao, Ruifen; Li, Ping; Ma, Xiao; Hu, Xueyi; Li, Fadong

doi:10.1016/j.eswa.2022.117351

Cited by 7 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several important studies were published in 2022. Zhou et al [98] automatically detected damages on railgun bore using deep learning and computer vision detection algorithms. Wei et al [99] measured the temporal-spatial distribution of rail transient temperatures for different launch speeds using an instantaneous temperature measurement system.…”

Section: ) System Optimization Stagementioning

confidence: 99%

Analysis of the Research Progress of Electromagnetic Railgun Based on CiteSpace

Mao,

Pang,

Guo

et al. 2024

IEEE Access

View full text Add to dashboard Cite

An electromagnetic railgun is a new type of weapon with ultrahigh speed based on electromagnetic thrust. It is used in important military domains, such as long-range strikes, strategic air defense, and ballistic interception. It is also used in aerospace fields, such as space debris cleanup, microsatellite launch, and space station relay launch, so its future prospects are excellent. We analyzed 1324 publications on electromagnetic railgun using CiteSpace and identified information including authors, institutions, journals, and popular research topics and trends. Our analysis found that the University of Texas is the research institution that has published the most documents on electromagnetic railgun, and French researcher Markus Schneider has published the most documents. The journals of IEEE Transactions on Magnetics and IEEE Transactions on Plasma Science have played an important role in promoting the development of electromagnetic railgun. The research direction of electromagnetic railgun has a distinct multidisciplinary characteristic because it touches on physics, engineering science, mathematics, materials science, energy and fuel, communication technology, instrumentation, and computer science. The development process of electromagnetic railgun can be grouped into three stages: basic theoretical research, engineering research, and system optimization research. The major research topics include electromagnetic force, armature design, rail materials, pulsed power supply, armature-rail contact surface characteristics, and coupling analysis of multiple physical fields. Future electromagnetic railgun will be scalable in terms of armature-rail contact characteristics and life span, energy storage density of power supply, current-carrying capability and thermal management of launcher materials, optimization design of armature structure, and complex data processing of control systems.

show abstract

Section: ) System Optimization Stagementioning

confidence: 99%

Analysis of the Research Progress of Electromagnetic Railgun Based on CiteSpace

Mao,

Pang,

Guo

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Unlike these indirect approaches, SOLOv2 [24] directly predicts leakage instance mask with the supervision of full instance mask annotations without dependence on bounding box detection. Although SOLOv2 has yet to be applied to tunnel projects, its successful applications of mechanical and medical fields provide references that justify its implementation for tunnel leakage identification [25,26].…”

Section: Overview Of Solov2mentioning

confidence: 99%

Improved SOLOv2 detection method for shield tunnel lining water leakages

Feng¹,

Zhang²,

Feng³

et al. 2023

Journal of Intelligent Construction

View full text Add to dashboard Cite

With the rapid development of urban metros, the detection of shield tunnel leakages has become an important research topic. Progressive technological innovations such as deep learning-based methods provide an effective way to detect tunnel leakages accurately and automatically. However, due to the complex shapes and sizes of leakages, it is challenging for existing algorithms to detect such defects. To address these issues, this paper proposes a novel deep learning-based model named segmenting objects by locations network v2 for tunnel leakages (SOLOv2-TL), which is enhanced by ResNeXt-50, deformable convolution, and path augmentation feature pyramid network (PAFPN). In the SOLOv2-TL, ResNeXt-50 coupled with deformable convolution is the backbone for boosting feature extraction ability that would enable the model sensitivity to leakages of different shapes. The PAFPN is introduced as the neck to reduce the loss of leakage information and more accurately assign leakages of different sizes to their corresponding feature levels. The superior performances of ResNeXt-50 with deformable convolution and PAFPN were validated by ablation tests. Moreover, the segmentation results obtained by SOLOv2-TL were compared with those by the mask region-based convolutional neural network (Mask R-CNN), Cascade Mask R-CNN, and SOLO which demonstrated that the mAP, mAP 50 , and mAP 75 of SOLOv2-TL are higher than those of the other methods, where mAP indicates the mean mask average precision (AP) at intersection over union (IoU) = 0.50:0.05:0.95, mAP 50 refers to the mean mask AP with an IoU threshold of 0.50, and mAP 75 denotes the mean mask AP with an IoU threshold of 0.75. Finally, a leakage area quantification method is presented.

show abstract

“…The geometric precision during the processing of deep hole components and the internal wall damage during usage directly affect their performance [1][2][3]. Only by obtaining the internal parameters of deep hole components can various geometric quantities such as diameter [4,5], roundness and straightness [6], and internal wall damage [7][8][9] be evaluated. Currently, the measurement of deep hole components overly relies on manual methods, with lever-type diameter measuring instruments commonly used for dimensional measurements and endoscopes for damage assessment.…”

Section: Introductionmentioning

confidence: 99%

Improving Measurement Accuracy of Deep Hole Measurement Instruments through Perspective Transformation

Zhao,

Du,

2024

Sensors

View full text Add to dashboard Cite

Deep hole measurement is a crucial step in both deep hole machining and deep hole maintenance. Single-camera vision presents promising prospects in deep hole measurement due to its simple structure and low-cost advantages. However, the measurement error caused by the heating of the imaging sensor makes it difficult to achieve the ideal measurement accuracy. To compensate for measurement errors induced by imaging sensor heating, this study proposes an error compensation method for laser and vision-based deep hole measurement instruments. This method predicts the pixel displacement of the entire field of view using the pixel displacement of fixed targets within the camera’s field of view and compensates for measurement errors through a perspective transformation. Theoretical analysis indicates that the perspective projection matrix changes due to the heating of the imaging sensor, which causes the thermally induced measurement error of the camera. By analyzing the displacement of the fixed target point, it is possible to monitor changes in the perspective projection matrix and thus compensate for camera measurement errors. In compensation experiments, using target displacement effectively predicts pixel drift in the pixel coordinate system. After compensation, the pixel error was suppressed from 1.99 pixels to 0.393 pixels. Repetitive measurement tests of the deep hole measurement instrument validate the practicality and reliability of compensating for thermal-induced errors using perspective transformation.

show abstract

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

Cited by 7 publications

References 33 publications

Analysis of the Research Progress of Electromagnetic Railgun Based on CiteSpace

Analysis of the Research Progress of Electromagnetic Railgun Based on CiteSpace

Improved SOLOv2 detection method for shield tunnel lining water leakages

Improving Measurement Accuracy of Deep Hole Measurement Instruments through Perspective Transformation

Contact Info

Product

Resources

About