基于时空卷积特征记忆模型的坦克火控系统视频目标检测方法

视频目标检测技术是提升坦克火控系统战场目标搜索能力的有效手段。针对面向坦克火控系统的视频目标检测任务，提出一种基于时空卷积特征记忆模型的视频目标检测方法。将时空卷积特征校准机制与卷积门控循环单元相结合，建立时空卷积特征记忆模型，同时对多个视频帧中目标的表观特征及运动信息进行建模，以传递并融合视频帧中的目标信息。在特征提取网络以及检测子网络中结合可形变卷积，在检测过程中应用视频序列非极大值抑制，提高对形变以及遮挡目标的检测能力。构建一个包含多种目标类型、尺度、遮挡等条件的坦克火控系统视频目标检测数据集，为多种目标检测方法的测试提供依据。测试结果表明，与R-FCN、 D&T以及MANet等目标检测方法相比，所提方法的平均精度均值最高，能够更好地满足装备的应用需求。

Video Object Detection Method for Tank Fire Control System Based on Spatial-temporal Convolution Feature Memory Model

Video object detection technology is an effective means to improve the battlefield object search capability of tank fire control system. In view of the video object detection task of tank fire control system，a video object detection method based on spatial-temporal convolution feature memory model is proposed. The spatial-temporal convolution feature alignment mechanism is combined with convolutional gated recurrent unit to construct a spatial-temporal convolution feature memory model，which can simultaneously model the apparent features and motion information of object in multiple video frames to transfer and fuse the object information in video frames. The feature extraction network and the detection sub-network are combined with the deformable convolution networks，and the non-maximum suppression of video sequences is used in the detection process to improve the performance of detection for deformed and occluded objects. A tank fire control system video object detection dataset is established，which considers different object types， scales，occlusions and other conditions，and can provide the basis for testing for different video object detection methods. The test results show that the mean average precision of the proposed method is the higher than those of R-FCN，D&T and MANet，and the proposed method can better meet the application requirements of equipment.