WU J H , LIU J . Velocity measurement of a highspeed underwater projectile with laser barriers [J ] . IEEE Access , 2021 , 9 : 61193 - 61199 .

孙嘉伟 , 弯港 , 顾金良 , 等 . 基于线阵相机的水下射弹动态参数及超空泡演化过程测量方法研究 [J ] . 兵工学报 , 2024 , 45 ( 8 ): 2564 - 2572 . DOI: 10.12382/bgxb.2023.0408 http://doi.org/10.12382/bgxb.2023.0408 针对水下环境恶劣、光线条件差、难以拍摄并复原高速射弹运动姿态及其超空泡演化过程等问题,采用线阵相机的交汇测量原理,设计基于线阵相机的水下射弹图像采集系统、搭建基于双线阵相机的图像采集靶面,提出一种针对水下高速超空泡目标识别与弹形复原算法。利用超空泡射弹几何关系与相机标定数据推导出射弹过靶坐标、姿态角与过靶速度公式,搭建12.7mm滑膛枪垂直发射入水实验,对系统的可行性进行验证分析。实验结果表明:该系统能够有效地进行水下射弹图像的快速采集,较完整的对弹形及空泡形态进行分离与复原,解算出射弹水下运动速度、姿态角及超空泡形态参数;系统解算出的速度与同时部署的高速摄像所处理的速度误差在1%以内,证明该系统具有较高的可靠性与实用性。

SUN J W , WAN G , GU J L , et al . Research on the measurement method of underwater projectile dynamic parameters and super vacuole evolution process based on line array camera [J ] . Acta Armamentarii , 2024 , 45 ( 8 ): 2564 - 2572 . (in Chinese)

侯宇 , 黄振贵 , 郭则庆 , 等 . 超空泡射弹小入水角高速斜入水试验研究 [J ] . 兵工学报 , 2020 , 41 ( 2 ): 332 - 341 . DOI: 10.3969/j.issn.1000-1093.2020.02.015 http://doi.org/10.3969/j.issn.1000-1093.2020.02.015 为研究超空泡射弹小入水角高速斜入水性能，利用高速摄像技术开展了超空泡射弹入水试验。沿水下弹道轨迹的左侧等距布置压力传感器测试压力变化，分析弹体不同侧滑角入水冲击过程的弹道轨迹、喷溅演变和水下压力波传播特征。结果表明：对于射弹小入水角高速斜入水，弹体小侧滑角入水能形成较光滑透明的入水空泡和稳定的入水弹道，较大的侧滑角易造成空泡内严重雾化、弹道轨迹偏转和弹体损坏等现象，严重程度随侧滑角增大而增大；小侧滑角下弹头空化器及圆锥段斜面与水面的撞击使得入水喷溅在俯视下呈左右近似对称的“蝶”状，侧滑角对前半部分喷溅的左右对称性影响较小，对后半部分喷溅的对称性和范围影响较大，前半部分喷溅的对称轴随着侧滑角增大会出现相应偏转；入水冲击产生的水下压力波变化过程可分成两阶段：由弹体和水域冲击产生的初始压力波动阶段和各压力波叠加的高频脉动阶段，两阶段压力波动因侧滑角增加后入水状态的不同而呈现不同的变化特征。

HOU Y , HUANG Z G , GUO Z Q , et al . Experimental study on high-speed oblique water entry of super-vacuum projectile with small water entry angle [J ] . Acta Armamentarii , 2020 , 41 ( 2 ): 332 - 341 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2020.02.015 http://doi.org/10.3969/j.issn.1000-1093.2020.02.015 The shallow-angle oblique water-entry of a high-speed supercavitating projectile is studied by using the high-speed photography technology. The characteristics of the ballistic trajectory, splash formation and underwater pressure wave propagation during the initial water-entry impact at different sideslip angles are observed and analyzed from the underwater pressure signal monitored along the left side of the trajectory. The results show that the small sideslip angle of the projectile has few effect on the smooth cavity formation and the enter trajectory stability during the high-speed oblique water-entry at small entry angle. The larger sideslip angle is able to lead to the severe water atomization in the cavity, the ballistic deflection and the projectile damage. The increase in the sideslip angle can exacerbate this instability. The impacts of the cavitator and the warhead conical section on the free surface at a small entry angle make the splash be an approximate symmetrical “butterfly shape” in birds-eye view. The sideslip angle has inverse influence on the symmetry of the front half of splash comparing with the rear half of splash. Large changes are presented in the splash range with the increase in sideslip angle. The symmetry axis of the front half of splash is deflected with the increase in sideslip angle. The changing process of underwater pressure wave induced by the water-entry impact is divided into two stages: the initial pressure fluctuation stage induced by the initial impact of the projectile on the water and the fluctuation stage of high frequency induced by the superposition of the pressure waves from various directions. The two pressure fluctuation stages show different variation characteristics with the corresponding water entry state due to the increased sideslip angle.Key

祁晓斌 , 施瑶 , 刘喜燕 , 等 . 阶梯式圆柱射弹小角度入水弹道特性研究 [J ] . 力学学报 , 2023 , 55 ( 11 ): 2468 - 2479 .

QI X B , SHI Y , LIU X Y , et al . Ballistic characterization of stepped cylindrical projectiles with small-angle water entry [J ] . Journal of Mechanics , 2023 , 55 ( 11 ): 2468 - 2479 . (in Chinese)

路丽睿 , 魏英杰 , 王聪 , 等 . 不同头型射弹低速倾斜入水空泡及弹道特性试验研究 [J ] . 兵工学报 , 2018 , 39 ( 7 ): 1364 - 1371 . DOI: 10.3969/j.issn.1000-1093.2018.07.014 http://doi.org/10.3969/j.issn.1000-1093.2018.07.014 为研究射弹头型对低速倾斜入水空泡及弹道特性的影响，基于高速摄像方法，开展不同头型射弹低速倾斜入水对比试验，得到了射弹头型对入水空泡、运动速度、俯仰角和阻力系数的影响规律。试验结果表明：同一入水时刻，空泡直径随着头部锥角增加而增大，半球头型射弹入水空泡直径小于锥角头型射弹；锥角头型射弹速度衰减速率随着锥角增加而增大，半球头型射弹速度衰减率小于锥角头型射弹；入水过程中半球头型射弹俯仰角变化最为剧烈，弹道稳定性也较差；不同头型射弹在入水过程中运动参数呈现出较强的非线性特性。

LU L R , WEI Y J , WANG C , et al . Experimental study on low-speed tilted water entry vacuoles and ballistic characteristics of different head-type projectiles [J ] . Acta Armamentarii , 2018 , 39 ( 7 ): 1364 - 1371 . (in Chinese)

EVERINGHAM M , VAN GOOL L , WILLIAMS C K I , et al . The pascal visual object classes (VOC) challenge [J ] . International Journal of Computer Vision , 2010 , 88 : 303 - 338 .

JIAO L C , ZHANG F , LIU F , et al . A survey of deep learning-based object detection [J ] . IEEE Access , 2019 , 7 : 128837 - 128868 . DOI: 10.1109/ACCESS.2019.2939201 http://doi.org/10.1109/ACCESS.2019.2939201 Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people's life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline thoroughly and deeply, in this survey, we analyze the methods of existing typical detection models and describe the benchmark datasets at first. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.

张侠 . 基于深度学习的高速目标检测与跟踪研究 [D ] . 西安 : 西安工业大学 , 2022 .

ZHANG X . Research on high-speed target detection and tracking based on deep learning [D ] . Xi’an : Xi’an University of Technology , 2022 . (in Chinese)

谢富 , 朱定局 . 深度学习目标检测方法综述 [J ] . 计算机系统应用 , 2022 , 31 ( 2 ): 1 - 12

XIE F , ZHU D J . A review of deep learning target detection methods [J ] . Computer System Applications , 2022 , 31 ( 2 ): 1 - 12 . (in Chinese)

ZHAO Z Q , ZHENG P , XU S , et al . Object detection with deep learning:a review [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 ( 11 ): 3212 - 3232 .

杜芸彦 , 杨锦辉 , 李鸿 , 等 . 基于改进Faster RCNN的少样本目标检测算法 [J ] . 电光与控制 , 2023 , 30 ( 5 ): 44 - 51 .

DU Y Y , YANG J H , LI H , et al . Sample less target detection algorithm based on improved Faster RCNN [J ] . Electro-Optics and Control , 2023 , 30 ( 5 ): 44 - 51 . (in Chinese)

LIU W , ANGUELOV D , ERHAN D , et al . S:Single shot multibox detector [C ] // Proceedings of the Computer Vision-ECCV 2016:14th European Conference . Amsterdam,the Netherlands : Springer , 2016 : 21 - 37 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once:unified,real-time object detection [C ] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,D.C.,US:IEEE , 2016 : 779 - 788 .

REDMON J , FARHADI A . YOLOv3:an incremental improvement:arXiv:1804.02767 [R ] . Ithaca,NY,US:Cornell University , 2018 :1804.02767.

BOCHKOVSKIY A , WANG C Y , LIAO H Y M . YOLOv 4:optimal speed and accuracy of object detection:arXiv:2004.10934 [R ] . Ithaca,NY,US:Cornell University , 2020 :2004.1093.

张旭 , 陈慈发 , 董方敏 . 基于改进YOLOv7的PCB缺陷检测算法 [J ] . 计算机工程 , 2024 , 50 ( 12 ): 318 - 328 . DOI: 10.19678/j.issn.1000-3428.0068588 http://doi.org/10.19678/j.issn.1000-3428.0068588 在PCB缺陷检测领域中检测精度的提高一直是1个具有挑战性的任务。为了解决这个问题, 提出一系列基于PCB缺陷检测的改进方法。首先, 引入一种新的注意力机制, 即BiFormer注意力机制, 这种机制利用双层路由实现动态的稀疏注意力, 从而减少计算量; 其次, 采用一种创新的上采样算子CARAFE, 能够结合语义信息与内容信息进行上采样, 使得上采样过程更加全面且高效; 最后, 基于MPDIoU度量采用一种新的损失函数, 即LMPDIoU损失函数, 能够有效地处理不平衡类别、小目标和密集性问题, 从而进一步提高图像检测的性能。实验结果表明, 所提改进后的模型在平均精度均值(mAP)方面取得了显著提高, 达到了93.91%, 与原YOLOv5模型相比提高了13.12个百分点, 同时, 在识别精度方面, 所提改进后的模型表现也非常出色, 达到了90.55%, 与原YOLOv5模型相比提高了8.74个百分点。引入BiFormer注意力机制、CARAFE上采样算子以及LMPDIoU损失函数, 对于提高PCB缺陷检测的精度和效率具有非常积极的作用, 为工业检测领域的研究提供了有价值的参考。

ZHANG X , CHEN C F , DONG F M . PCB defect detection algorithm based on improved YOLOv7 [J ] . Computer Engineering , 2024 , 50 ( 12 ): 318 - 328 . (in Chinese) DOI: 10.19678/j.issn.1000-3428.0068588 http://doi.org/10.19678/j.issn.1000-3428.0068588 Achieving enhanced detection accuracy is a challenging task in the field of PCB defect detection. To address this problem, this study proposes a series of improvement methods based on PCB defect detection. First, a novel attention mechanism, referred to as BiFormer, is introduced. This mechanism uses dual-layer routing to achieve dynamic sparse attention, thereby reducing the amount of computation required. Second, an innovative upsampling operator called CARAFE is employed. This operator combines semantic and content information for upsampling, thereby making the upsampling process more comprehensive and efficient. Finally, a new loss function based on the MPDIoU metric, referred to as the LMPDIoU loss function, is adopted. This loss function effectively addresses unbalanced categories, small targets, and denseness problems, thereby further improving image detection performance. The experimental results reveal that the model achieves a significant improvement in mean Average Precision (mAP) with a score of 93.91%, 13.12 percentage points higher than that of the original model. In terms of recognition accuracy, the new model reached a score of 90.55%, representing an improvement of 8.74 percentage points. These results show that the introduction of the BiFormer attention mechanism, CARAFE upsampling operator, and LMPDIoU loss function effectively improves the accuracy and efficiency of PCB defect detection. Thus, the proposed methods provide valuable references for research in industrial inspection, laying the foundation for future research and applications.

SHANG Z F , WANG C P . A detection algorithm for projectile target [J ] . Advanced Materials Research , 2014 , 1044 : 972 - 975 .

唐云祁 , 李同 , 丁建伟 , 等 . 弹种自动识别算法研究与实现 [J ] . 中国人民公安大学学报(自然科学版) , 2016 , 22 ( 1 ): 24 - 26 .

TANG Y Q , LI T , DING J W , et al . Research and implementation of automatic identification algorithm of bullet species [J ] . Journal of Chinese People’s Public Security University (Natural Science Edition) , 2016 , 22 ( 1 ): 24 - 26 . (in Chinese)

QIU Q X , LI P F . Research on bullet recognition technology based on deep learning [C ] // Proceedings of International Conference on Energy Storage and Intelligent Vehicles.Singapore:Springer , 2022 : 678 - 687 .

WANG C Y , BOCHKOVSKIY A , LIAO H Y M . YOLOv7:trainable bag-of-freebies sets new state-of-the-art for real-time object detectors [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,D.C.,US:IEEE , 2023 : 7464 - 7475 .

HOU Q B , ZHOU D Q , FENG J S . Coordinate attention for efficient mobile network design [C ] // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,D.C.,US:IEEE , 2021 : 13713 - 13722 .

杨环宇 , 王军 , 吴祥 , 等 . 一种坐标通道注意力深度学习网络的军用飞机识别方法 [J ] . 兵工学报 , 2024 , 45 ( 7 ): 2128 - 2143 . DOI: 10.12382/bgxb.2023.0526 http://doi.org/10.12382/bgxb.2023.0526 战场态势瞬息万变,利用可见光图像对敌方用于军事行动的飞机类型进行有效区分,对提供军事作战信息具有重要意义。针对现有军用飞机识别方法存在小目标飞机和环境背景复杂导致的模型特征提取困难、数据样本数量不足导致的模型训练不充分的问题,提出一种坐标通道注意力(ConvNeXt-Coordinate Attention,ConvNeXt-CA)深度学习网络军用飞机目标识别方法。该方法在ConvNeXt网络可以保留小目标飞机特征的基础上,引入CA机制设计CA-Stage模块,提升网络对于背景和前景的区分能力;采用数据增强的方式扩充数据集,以及使用迁移学习的策略提高模型的泛化能力,训练得到具备最优超参数的ConvNeXt-CA网络。实验结果表明,与传统的军用飞机识别方法和其他深度学习模型相比,基于迁移学习的ConvNeXt-CA网络在预测准确率上有明显的提升,且具备较强的泛化能力。

YANG H Y , WANG J , WU X , et al . A coordinate channel attention deep learning network for military aircraft recognition [J ] . Acta Armamentarii , 2024 , 45 ( 7 ): 2128 - 2143 . (in Chinese)