孟祥盛 , 李乐堃 . 数字化激光扫描成像引信低空海面背景目标识别方法 [J ] . 红外与激光工程 , 2023 , 52 ( 4 ): 309 - 320 .

MENG X S , LI L K . Target recognition method of digital laser imaging fuze in ultra-low sea background [J ] . Infrared and Laser Engineering , 2023 , 52 ( 4 ): 309 - 320 . (in Chinese)

郑震 , 查冰婷 , 张合 . 基于DHGF算法的激光线扫描成像引信目标识别方法 [J ] . 中国激光 , 2018 , 45 ( 7 ): 147 - 154 .

ZHENG Z , ZHA B T , ZHANG H . Target recognition method of laser line scanning imaging fuze based on DHGF algorithm [J ] . Chinese Journal of Lasers , 2018 , 45 ( 7 ): 147 - 154 . (in Chinese)

霍健 , 陈慧敏 , 马云飞 , 等 . 基于MEMS激光雷达的车辆目标识别算法 [J ] . 兵工学报 , 2023 , 44 ( 4 ): 940 - 948 .

HUO J , CHEN H M , MA Y F , et al. Vehicle target recognition algorithm based on MEMS LiDAR [J ] . Acta Armamentarii , 2023 , 44 ( 4 ): 940 - 948 . (in Chinese) DOI: 10.12382/bgxb.2021.0822 http://doi.org/10.12382/bgxb.2021.0822 In order to solve the problem of low recognition accuracy of traditional linear array Lidar, recognition algorithm based on MEMS LiDAR pushbroom scanning is designed. To reduce the amount of computation, directly filtering and grid segmentation algorithms are introduced to reduce the amount of original point clouds and effectively improve the real-time performance of detection. Combined with the organized processing method of MEMS LiDAR point cloud, a point cloud clustering algorithm based on mathematical morphology is proposed, which divides the point clouds after removing the ground points into independent point cloud clusters. The denoising algorithm based on distribution histogram with adaptive threshold is used to remove the outlier noise points around the targets. On this basis, a multifeature composite criterion is designed to directly process the three-dimensional LiDAR point clouds after clustering denoising to realize the accurate recognition of the targets. The data processing results under different experimental conditions are analyzed, and the recognition accuracy reaches 94.9%, which shows that the method has good generalization ability and accuracy.

周瑜 , 贺伟 . 激光成像引信的目标识别方法研究 [J ] . 激光技术 , 2023 , 47 ( 2 ): 267 - 272 .

ZHOU Y , HE W . Target recognition method of laser imaging fuze [J ] . Laser Technology , 2023 , 47 ( 2 ): 267 - 272 . (in Chinese)

武军安 , 郭锐 , 刘荣忠 , 等 . 用于弹载线阵红外与激光扫描成像引信的轻量化卷积神经网络目标识别方法 [J ] . 弹道学报 , 2021 , 33 ( 3 ): 89 - 96 . DOI: 10.12115/j.issn.1004-499X(2021)03-014 http://doi.org/10.12115/j.issn.1004-499X(2021)03-014 为了进一步提高末敏弹在复杂战场环境下对地面装甲目标的识别概率,提出了一种基于轻量化卷积神经网络的红外图像与距离像复合探测识别方法。网络设计考虑了弹载环境对实时性的要求,将网络划分为特征提取、特征融合2个阶段。在特征提取阶段,对不同源的图像进行了分布式卷积,提高了网络的并行性,降低了网络参数量与计算量; 为了弥补分布式卷积带来的特征损失,将距离像与红外图像的融合图像也一并作为网络输入; 在特征融合阶段利用深度可分离卷积实现了进一步的轻量化设计。通过仿真缩比实验环境获得的数据集进行实验验证,实验结果表明,网络具有较小计算复杂度的同时能够对复杂背景环境下的装甲目标进行有效识别。

WU J A , GUO R , LIU R Z , et al. A lightweight convolutional neural network target recognition method for missile-borne fuse linear array infrared and lidar-scanning image [J ] . Journal of Ballistics , 2021 , 33 ( 3 ): 89 - 96 . (in Chinese)

郭甲崇 , 刘星 , 袁俊 , 等 . 激光/红外复合扫描引信目标识别算法 [J ] . 激光与红外 , 2020 , 50 ( 2 ): 184 - 191 .

GUO J C , LIU X , YUAN J , et al. Target recognition algorithm for laser/infrared composite scanning fuze [J ] . Laser & Infrared , 2020 , 50 ( 2 ): 184 - 191 (in Chinese).

张远利 . 短波红外成像技术在军事上的应用进展 [J ] . 应用激光 , 2024 , 44 ( 4 ): 171 - 177 .

ZHANG Y L . Advancements in the military applications of short-wave infrared imaging technology [J ] . Applied Laser , 2024 , 44 ( 4 ): 171 - 177 (in Chinese).

BUSTOS N , MASHHADI M , LAI-YUEN S K , et al. A systematic literature review on object detection using near infrared and thermal images [J ] . Neurocomputing , 2023 ,560:126804.

许建康 , 刘丙新 , 杜雨隆 , 等 . 船舶溢油污染海冰可见光-近红外反射光谱特征及其角度效应 [J ] . 光谱学与光谱分析 , 2024 , 44 ( 7 ): 2075 - 2082 .

XU J K , LIU B X , DU Y L , et al. Visible near-infrared reflection spectrum characteristics and angular effects of sea ice contaminated by ship oil spills [J ] . Spectroscopy and Spectral Analysis , 2024 , 44 ( 7 ): 2075 - 2082 (in Chinese).

KARANGWA J , LIU J , ZENG Z X . Vehicle detection for autonomous driving:a review of algorithms and datasets [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 11 ): 11568 - 11594 .

熊光明 , 罗震 , 孙冬 , 等 . 基于红外相机和毫米波雷达融合的烟雾遮挡无人驾驶车辆目标检测与跟踪 [J ] . 兵工学报 , 2024 , 45 ( 3 ): 893 - 906 . DOI: 10.12382/bgxb.2022.0602 http://doi.org/10.12382/bgxb.2022.0602 战场环境下无人驾驶车辆的感知系统易受烟雾、扬尘等天气的影响,对关键目标的检测与跟踪能力大大下降,造成目标漏检、目标误检、目标丢失等严重后果。针对该问题,开发毫米波雷达和红外相机融合系统,采用目标级融合方式建立简洁有效的融合规则,提炼和组合各传感器的优势信息,最终输出稳定的目标感知结果。对毫米波雷达的目标进行有效性检验和提取,并提出改进的基于密度的含噪声空间聚类应用算法,以减少毫米波雷达噪音干扰。以YOLOv4网络为基础,引入MobileNetv2主干网络,在网络训练过程中运用迁移学习方法,同时对红外数据样本进行扩充,解决了红外图像训练样本少的问题。试验结果表明,相较于仅基于红外相机的算法,融合检测算法在烟雾环境下的精度显著提升,且算法实时性高,实现了烟雾环境下毫米波雷达与红外相机融合的目标检测与跟踪,提高了无人驾驶车辆目标检测与跟踪系统的抗烟雾干扰能力。

XIONG G M , LUO Z , SUN D , et al. Object detection and tracking for unmanned vehicles based on fusion of infrared camera and MMW radar in smoke-obscured environment [J ] . Acta Armamentarii , 2024 , 45 ( 3 ): 893 - 906 . (in Chinese) DOI: 10.12382/bgxb.2022.0602 http://doi.org/10.12382/bgxb.2022.0602 In the battlefield environment, the perception system of unmanned vehicle is susceptible to the influence of weather such as smoke and dust. The ability to detect and track key objects is greatly reduced under harsh weather conditions, resulting in serious consequences, such as object miss-detection, object misdetection and object missing. To address this problem, a fusion system of MMW radar and infrared camera is developed. The object-level fusion method is adopted to establish simple and effective fusion rules, extract and combine the dominant information from each sensor, and finally output stable objective perception results. The objects of MMW radar are checked and extracted. And an improved DBSCAN clustering algorithm is proposed to reduce the noise of MMW radar. The MobileNetv2 backbone network is introduced based on the YOLOv4 network. In the process of network training, the transfer learning method is used to expand the infrared data samples, which solves the problem of fewer training samples of infrared images. The experimental results show that the fusion algorithm has significantly better accuracy and high real-time performance in the smoke environment compared with the algorithm based on infrared camera only, which realizes the object detection and tracking of the fusion of MMW radar and infrared camera in the smoke environment, and improves the anti-interference ability of the object detection and tracking system of unmanned vehicles.

SINDAGI V A , ZHOU Y , TUZEL C , et al. MVX-Net:multimodal VoxelNet for 3D object detection [C ] //Proceedings of 2019 International Conference on Robotics and Automation.Montreal , Canada:IEEE ,2019: 7276 - 7282 .

VORA S , LANG A H , HELOU B , et al. PointPainting:sequential fusion for 3D object detection [C ] //Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle,WA , US:IEEE ,2020: 4603 - 4611 .

WANG C W , MA C , ZHU M , et al. PointAugmenting:cross-modal augmentation for 3D object detection [C ] //Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Online:IEEE ,2021: 11789 - 11798 .

CHEN X Z , MA H M , WAN J , et al. Multi-view 3D object detection network for autonomous driving [C ] //Proceedings of 2017 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Honolulu,HI , US:IEEE ,2017: 6526 - 6534 .

KU J , MOZIFIAN M , LEE J , et al. Joint 3D proposal generation and object detection from view aggregation [C ] //Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems.Madrid , Spain:IEEE ,2018: 5750 - 5757 .

PANG S , MORRIS D , RADHA H . CLOCs:camera-liDAR object candidates fusion for 3D object detection [C ] //Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems.Las Vegas,NV , US:IEEE ,2020: 10386 - 10393 .

LI Y W , YU A W , MENG T J , et al. DeepFusion:lidar-camera deep fusion for multi-modal 3D object detection [C ] // Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.New Orleans,LA , US:IEEE ,2022: 17161 - 17170 .

BAI X Y , HU Z Y , ZHU X G , et al. TransFusion:Robust LiDAR-camera fusion for 3D object detection with Transformers [C ] //Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.New Orleans,LA , US:IEEE ,2022: 1080 - 1089 .

LIANG T T , XIW H W , YU K C , et al. BEVFusion:a simple and robust LiDAR-camera fusion framework [C ] //Proceedings of Advances in Neural Information Processing Systems.New Orleans,LA , US:NIPS ,2022:35.

LANG A H , VORA S , CAESAR H , et al. PointPillars:Fast encoders for object detection from point clouds [C ] //Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Long Beach,CA , US:IEEE ,2019: 12689 - 12697 .

YAN Y , MAO Y X , LI B . SECOND:Sparsely embedded convolutional detection [J ] . Sensors , 2018 , 18 ( 10 ): 3337 .

LIU Z , LIN Y T , CAO Y , et al. Swin transformer:Hierarchical vision transformer using shifted windows [C ] //Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal, Canada:IEEE , 2021 , 9992 - 10002 .

PHILION J , FIDLER S . Lift,splat,shoot:encoding images from arbitrary camera rigs by implicitly unprojecting to 3D [C ] //Proceedings of 16th European Conference on Computer Vision. Glasgow, UK:Springer , 2020 ,12359: 194 - 210 .

GEIGER A , LENZ P , URTASUN R . Are we ready for autonomous driving? The KITTI vision benchmark suite [C ] //Proceedings of 2012 IEEE/CVF International Conference on Computer Vision. Providence,RI , US:IEEE ,2012: 3354 - 3361 .