李良福 , 陈卫东 , 高强 , 等 . 基于深度学习的光电系统智能目标识别 [J ] . 兵工学报 , 2022 , 43 ( 增刊1 ) : 162 - 168 .

LI L F , CHEN W D , GAO Q , et al . Deep learning-based intelligent target recognition technology for electro-optical system [J ] . Acta Armamentarii , 2022 , 43 ( S1 ): 162 - 168 . (in Chinese) DOI: 10.12382/bgxb.2022.A004 http://doi.org/10.12382/bgxb.2022.A004 Intelligent target recognition technology is an important support of multi-dimensional and three-dimensional reconnaissance system of electro-optical system, which is the basis of multi-angle and omni-directional target positioning and perception analysis. Focusing on the three challenges of data，algorithm and computing power，an intelligent target recognition method based on multi-source information fusion is proposed to meet the needs of deep learning-based target recognition of electro-optical system in complex environment.The proposed method is to learn and train the images fused by multiple sensors so as to improve the ability to recognize targets.The target recognition technology based on multi-dimensional image fusion is used to label，train and learn the multi band fused image data，thus automatically identifying the multiple targets in the image. Experimental results show that the proposed method can be used to accurately identify and locate the fusion target.

李博 , 王博 , 韩京冶 , 等 . 基于车载计算机的红外图像移动目标检测 [J ] . 兵工学报 , 2022 , 43 ( 增刊1 ): 66 - 73 .

LI B , WANG B , HAN J Y , et al . Infrareds image moving object detection technology based on onboard computer [J ] . Acta Armamentarii , 2022 , 43 ( S1 ): 66 - 73 . (in Chinese)

于博文 , 吕明 . 改进的YOLOv3 算法及其在军事目标检测中的应用 [J ] . 兵工学报 , 2022 , 43 ( 2 ): 345 - 354 . DOI: 10.3969/j.issn.1000-1093.2022.02.012 http://doi.org/10.3969/j.issn.1000-1093.2022.02.012 复杂环境下军事目标检测技术是提高战场态势生成、分析能力的基础和关键。针对军事目标检测任务在复杂环境下传统检测算法的检测性能较低问题，提出一种基于改进YOLOv3的军事目标检测算法，通过深度学习实现复杂环境下军事目标的自动检测。构建军事目标图像数据集，为各类目标检测算法提供测试环境；在网络结构上通过引入可形变卷积改进的ResNet50-D残差网络作为特征提取网络，提高网络对形变目标的检测精度和速度；在特征融合阶段引入双注意力机制和特征重构模块，增强目标特征的表征能力，抑制干扰，提升检测精度；利用DIOU损失函数和Focal损失函数重新设计目标检测器的损失函数，进一步提高其对军事目标的检测精度；在军事目标图像数据集中进行测试实验。实验结果表明，改进的YOLOv3算法相比于原YOLOv3算法，平均精度均值提高了2.98%，检测速度提高了8.6帧/s，具有较好的检测性能，可为战场态势生成、分析提供有效的辅助技术支持。

YU B W , LÜ M . Improved YOLOv3 algorithm and its application in military target detection [J ] . Acta Armamentarii , 2022 , 43 ( 2 ): 345 - 354 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2022.02.012 http://doi.org/10.3969/j.issn.1000-1093.2022.02.012 Military target detection in a complex environment is the basis and key to improving battlefield situation generation and analysis capability. For the military target detection tasks, the detection performance of traditional detection algorithms in complex environment is low. A military target detection algorithm based on improved YOLOv3 algorithm is proposed to automatically detect the military targets in complex environment through deep learning. A military target image dataset is constructed to provide a testing environment for various target detection algorithms. The detection accuracy and speed of deformable target are improved by introducing the deformable convolutional improved ResNet50-D residual network as feature extraction network. In the stage of feature fusion, a dual-attention mechanism and feature reconstruction module are introduced to enhance the characterization ability of target features, suppress the interference, and improve the detection accuracy. The loss function of target detector is redesigned by using DIOU Loss functions and Focal Loss to funther improve the detection accuracy of military targets. The experimental results show that the improved YOLOv3 algorithm improves the average detection accuracy by 2.98% and the detection speed by 8.6 frames/s compared with the original YOLOv3 algorithm. The improved YOLOv3 algorithm has better detection performance and can provide effective auxiliary technical support for battlefield situation generation and analysis.

杨传栋 , 钱立志 , 薛松 , 等 . 图像自寻的弹药目标检测方法综述 [J ] . 兵工学报 , 2022 , 43 ( 10 ): 2687 - 2704 .

YANG C D , QIAN L Z , XUE S , et al . Review on target detection of image homing ammunition [J ] . Acta Armamentarii , 2022 , 43 ( 10 ): 2687 - 2704 . (in Chinese) DOI: 10.12382/bgxb.2021.0610 http://doi.org/10.12382/bgxb.2021.0610 The onboard image target detection method is the key technology to realize the autonomous attack on the target by the “fire-and-forget” image homing ammunition. At present, the image homing of ammunition is faced with some problems, such as bad imaging environment, rapid change of targets' characteristics, and strict requirements for algorithm volume and speed. Firstly, the target detection methods based on deep learning are divided into methods based on anchor box, methods without anchor box and methods based on transformer, and the main technical progress of various methods is reviewed. Then, the key technologies in onboard image target detection model deployment, such as lightweight feature extraction network, enhancement of feature map for prediction, non-maximum suppression post-processing algorithm, sample equalization in training, and model compression, are studied. Finally, the performances of the typical detection algorithms on ImageNet, COCO and datasets for onboard image are compared, and the possible development in the future is looked into.

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董光辉 , 陈星宇 . YOLOv5定位多特征融合的车标识别 [J ] . 计算机工程与应用 , 2023 , 59 ( 5 ): 176 - 193 . DOI: 10.3778/j.issn.1002-8331.2207-0389 http://doi.org/10.3778/j.issn.1002-8331.2207-0389 为解决智能交通系统中车标识别的问题，提出YOLOv5s网络车标定位多特征融合的车标图像识别方案。车标定位阶段选择YOLOv5s网络以满足对车标定位速度与精度等的需求。车标识别阶段通过调整扩展高斯差分中的参数得到具有不同效果的车标边缘，设计一组二维Gabor滤波器对边缘检测后的车标图像进行滤波处理并提取出对应的车标图像特征向量，通过计算待测车标图像特征与标准比对库中特征向量的欧几里德距离，取距离最小者对应的标签索引作为分类识别结果，该方案的最佳识别正确率为96.91%。采用随机森林算法进行分类后的最佳识别正确率可达99.33%。该方案的车标定位与识别最佳整体正确率超过了YOLOv5s网络直接一步到位识别车标的方案，且相较于传统图像处理方法有明显提升。

DONG G H , CHEN X Y . Vehicle logo recognition with YOLOv5 location and multi-feature fusion [J ] . Computer Engineering and Applications , 2023 , 59 ( 5 ): 176 - 193 . (in Chinese) DOI: 10.3778/j.issn.1002-8331.2207-0389 http://doi.org/10.3778/j.issn.1002-8331.2207-0389 In order to solve the problem of vehicle logo identification in intelligent transportation system, a vehicle logo image recognition scheme based on using YOLOv5s network to locate vehicle logo and multi-feature fusion is proposed. The YOLOv5s network is selected in the vehicle logo positioning stage to meet the requirements of the positioning speed and accuracy. Firstly, in the vehicle logo recognition stage, the vehicle logo edges with different effects are obtained by adjusting the parameters in the extended difference of Gaussians. Then, a set of two-dimensional Gabor filters are designed to filter the edge-detected vehicle logo image and extract the corresponding feature vector. Finally, by calculating the Euclidean distance between the feature vector of the vehicle logo image to be tested and the feature vector in the standard comparison library, the label index corresponding to the smallest distance is taken as the classification and recognition result. The optimal recognition accuracy of the scheme is 96.91%, which can reach 99.33% after classification with random forest algorithm. The best overall accuracy of vehicle logo positioning and recognition in this paper exceeds that by using the YOLOv5s network in one step, and it is significantly improved compared with the traditional image processing method.

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