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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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GONG S X , WANG X , KONG G J , et al . Methods for multi-vehicle cooperative object tracking [J ] . Acta Armamentarii , 2022 , 43 ( 10 ): 2429 - 2442 . (in Chinese) DOI: 10.12382/bgxb.2021.0462 http://doi.org/10.12382/bgxb.2021.0462 Multi-vehicle information fusion technology is an important way to improve the perception of the environment of ground unmanned systems. To address the problem of discontinuous and unstable object tracking in single-vehicle sensors caused by vision occlusion and blind spots, a result-level fusion system model for centralized multi-vehicle cooperative perception is proposed. The system model uses lidar as the vehicle perception sensor and stands on the D-S evidence theory to fuse the environment grid maps constructed by different vehicles at the main control terminal to obtain a global static environment map. Based on this environment model, a multi-vehicle cooperative object detection and tracking method is designed. First, a maximum value suppression method is used to resolve the fusion conflict of detected objects. Then, a cascaded dynamic object matching and tracking management method is designed to complete object prediction and tracking and send the results to vehicles. The test results of a real-vehicle system composed of two unmanned vehicles suggest that when the object is occluded, the proposed multi-vehicle cooperative object detection and tracking architecture can obtain more comprehensive environmental information of the object than a single-vehicle perception system. No tracking object is missed, and no jump occurs. The error between the tracker's output position state result and the detection result is small. The state of the tracked object can be accurately estimated, and the tracking trajectory remains continuous, thus effectively improving the field of vision of the single-vehicle environment.

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HE K M , ZHANG X Y , REN S Q , et al . Spatial pyramid pooling in deep convolutional networks for visual recognition [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015 , 37 ( 9 ): 1904 - 1916 . DOI: 10.1109/TPAMI.2015.2389824 http://doi.org/10.1109/TPAMI.2015.2389824 Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

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张达为 , 刘绪崇 , 周维 , 等 . 基于改进YOLOv3的实时交通标志检测算法 [J ] . 计算机应用 , 2022 , 42 ( 7 ): 2219 - 2226 . DOI: 10.11772/j.issn.1001-9081.2021050731 http://doi.org/10.11772/j.issn.1001-9081.2021050731 针对目前我国智能驾驶辅助系统识别道路交通标志检测速度慢、识别精度低等问题，提出一种基于YOLOv3的改进的道路交通标志检测算法。首先，将MobileNetv2作为基础特征提取网络引入YOLOv3以形成目标检测网络模块MN-YOLOv3，在MN-YOLOv3主干网络中引入两条Down-up连接进行特征融合，从而减少检测算法的模型参数，提高了检测模块的运行速度，增强了多尺度特征图之间的信息融合；然后，根据交通标志目标形状的特点，使用K-Means++算法产生先验框的初始聚类中心，并在边界框回归中引入距离交并比（DIOU）损失函数来将DIOU与非极大值抑制（NMS）结合；最后，将感兴趣区域（ROI）与上下文信息通过ROI Align统一尺寸后融合，从而增强目标特征表达。实验结果表明，所提算法性能更好，在长沙理工大学中国交通标志检测（CCTSDB）数据集上的平均准确率均值（mAP）可达96.20%。相较于Faster R-CNN、YOLOv3、Cascaded R-CNN检测算法，所提算法拥有具有更好的实时性和更高的检测精度，对各种环境变化具有更好的鲁棒性。

ZHANG D W , LIU X C , ZHOU W , et al . Real-time traffic sign detection algorithm based on improved YOLOv3 [J ] . Journal of Computer Applications , 2022 , 42 ( 7 ): 2219 - 2226 . (in Chinese) DOI: 10.11772/j.issn.1001-9081.2021050731 http://doi.org/10.11772/j.issn.1001-9081.2021050731 Aiming at the problems of slow detection and low recognition accuracy of road traffic signs in Chinese intelligent driving assistance system， an improved road traffic sign detection algorithm based on YOLOv3 （You Only Look Once version 3） was proposed. Firstly， MobileNetv2 was introduced into YOLOv3 as the basic feature extraction network to construct an object detection network module MN-YOLOv3 （MobileNetv2-YOLOv3）. And two Down-up links were added to the backbone network of MN-YOL Ov3 for feature fusion， thereby reducing the model parameters， and improving the running speed of the detection module as well as information fusion performance of the multi-scale feature maps. Then， according to the shape characteristics of traffic sign objects， K -Means++ algorithm was used to generate the initial cluster center of the anchor， and the DIOU （Distance Intersection Over Union） loss function was introduced to combine DIOU and Non-Maximum Suppression （NMS） for the bounding box regression. Finally， the Region Of Interest （ROI） and the context information were unified by ROI Align and merged to enhance the object feature expression. Experimental results show that the proposed algorithm has better performance， and the mean Average Precision （mAP） of the algorithm on the dataset CSUST （ChangSha University of Science and Technology） Chinese Traffic Sign Detection Benchmark （CCTSDB） can reach 96.20%. Compared with Faster R-CNN （Region Convolutional Neural Network）， YOLOv3 and Cascaded R-CNN detection algorithms， the proposed algorithm has better real-time performance， higher detection accuracy， and is more robustness to various environmental changes.

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