ID 原文 译文
40006 针对以上问题，研究了一种基于改进型RetinaNet的三维人体目标实时检测方法， In order to solve the above problem, this paper studies a real-time detection method of 3-D human targets based on improved RetinaNet.
40007 将主干网络与特征金字塔网络结合用于雷达点云和图像特征的提取，并将两者融合的特征锚框输入到功能网络从而输出三维边界框和目标类别信息。 The backbone network and feature pyramid network are combined for point cloud and image feature extraction, and the fused feature anchors are input into the functional network to output the 3-D boundary boxes and target category information.
40008 该方法采用单阶段网络结构直接回归目标的类别概率和位置坐标值，并且通过引入聚焦损失函数解决单阶段网络训练过程中存在的正负样本不平衡问题。 By using the one-stage network structure, the method directly regresses the category probability and position coordinates of the targets, solving the imbalance problem of positive and negative samples in the process of one-stage network training by introducing focal loss function.
40009 在KITTI数据集上进行的实验表明，本文方法在三维人体目标检测的平均精度和耗时方面均优于对比算法，可有效实现目标检测的准确性和实时性之间的平衡。 Experiments on KITTI dataset show that the proposed method outperforms the contrast algorithms in terms of average accuracy and time-consuming, and can effectively balance the accuracy and real-time performance of target detection.
40010 针对目前图像配准算法对于多重复纹理图像配准位置偏差的问题，提出图像内自匹配与图像间互匹配相结合的双匹配配准（Double-match image registration，DMIR）算法。 To solve the problem of the registration position deviation for multi-repeat texture images, a double-match image registration(DMIR)algorithm is proposed. The DMIR algorithm not only considers the matching result of one graph with another graph, but also considers the matching result of a graph with its own features.
40011 首先在对待匹配图像提取尺度不变特征转换（Scale-invariant feature transform，SIFT）特征之后，通过K-近邻（K-nearest neighbor，KNN）算法进行特征匹配， Firstly, the key points are matched by the K-nearest neighbor(KNN) algorithm after extracting the feature points by the scale-invariant feature transform(SIFT)algorithm.
40012 分别得到同一张图片的自匹配点对和不同图像间的初始互匹配点对； As a result, the selfmatching point pairs of the same image and the initial matching point pairs between different images are obtained respectively.
40013 然后对初始互匹配点对进行相关性计算得到最正确的匹配点对， Secondly, the best matching point pairs are obtained by computing the correlation between different points of the initial matching point pairs.
40014 并根据最正确的匹配点对与自匹配点对的位置关系确定更多的正确匹配点对， Thirdly, the correct matching point pairs of the two images are determined, which depend on the positional relationship between the best matching point pairs and the self-matching point pairs.
40015 最后计算仿射矩阵对图像进行拼接。 Lastly, the affine matrix is calculated according to the matching point pairs, and the image stitching is performed.