Search

The spatial location of fuze detonation point is crucial for evaluating the working condition and improving the performance of fuze. Considering the observation safety, the non-contact long-distance accurate measurement technology is essential. In this paper, we propose a method that takes the sensor data of optics, spatial attitude and GPS as input and outputs the spatial position of the fuze detonation point. The proposed method consists of two steps. First, an object detection algorithm with post-processing algorithm is proposed to obtain rich information of the target. The algorithm achieves high-accuracy detection by introducing powerful backbone, attention mechanism, group convolution, and improved multi-scale feature fusion. Second, a Variational Auto-Encoder (VAE) algorithm model improved by dense connection structure and multiple source heterogeneous sensor information fusion structure is proposed as the position regression algorithm. It receives the status information of the observer camera and the output of the object detection algorithm, and then outputs the three-dimensional coordinates of the explosion point. Finally, method validation and performance analysis are realized through virtual scene simulation. Experiment results show the superiority of the proposed object detection algorithm over other typical algorithms on explosion flare detection, with its Average Precision (AP) of 0.889. The positioning error of the spatial location method is 0.896 m, while that of the binocular stereo vision method is 2.863 m. Therefore, the proposed target spatial location method is proved to be effective and accurate. [ABSTRACT FROM AUTHOR]

Highlights • Method simulates effects of launch conditions on rail-gun current and armature speed. • The input five parameters are closely related to the rail-gun launchment. • Three neural networks predict the rail-gun current and armature speed with low error. • General Regression neural network is optimal on error, time cost, parameter design. Abstract In this paper, based on the advantages of artificial neural network, such as good tolerance of data noise, strong ability of nonlinear mapping, multi-dimensional input variables, fast operation, low error, etc., a method of using artificial neural network for data prediction is proposed for the research of rail-gun. The results show that it is feasible to use the Back Propagation Neural Network, the Radial Basis Function Neural Network and the General Regression Neural Network to realize the method of prediction and simulation of the rail-gun current and the armature speed curve through relevant parameters. The General Regression Neural Network has superiority in error performance and time cost of neural network training and simulation. [ABSTRACT FROM AUTHOR]

• 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. 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. [ABSTRACT FROM AUTHOR]