ID 原文 译文
38826 近年来，目标检测已经在含有大量标注的数据上展现出了良好的效果，但当真实测试数据与标注数据存在域间差异时，往往会导致训练好的目标检测模型性能降低。 In recent years, object detection has shown great performance with large quantities of labeled data, but when a domain discrepancy occurs between the real test data and the labeled data, the performance of a trained object detection model often decreases.
38827 由于相比于自然图像，多源遥感图像在成像方式和分辨率等方面存在特有的差异，而传统的方法需要将多源图像数据重新标注，这将消耗大量人力和时间，因此在遥感图像上实现自适应目标检测面临特有的挑战。 Compared with natural images, multi-source remote sensing images have unique discrepancies in imaging methods and resolutions. Traditional methods needed to re-label multi-source images, which spent lots of manpower and time. Therefore, it faced unique challenges to implement adaptive object detection for remote sensing images.
38828 针对以上问题，本文提出了一种面向多源遥感图像的自适应目标检测算法，在图像级别和语义级别上对网络进行对抗训练。 In view of the above problems, this paper proposed an adaptive object detection algorithm for multi-source remote sensing images, which conducted adversarial training at the image level and semantic level.
38829 此外，通过结合超分辨网络，进一步缩小了图像级别的差异，实现了自适应目标检测。 In addition, by combining super-resolution networks, we further alleviated the discrepancy at the image level and realized adaptive object detection.
38830 本文在两个多源遥感数据集上进行实验，结果表明本文方法有效提升了目标域上的检测效果。 We conducted experiments on two multi-source remote sensing image datasets, and the results show that the proposed method effectively improves detection performance on the target domain.
38831 针对现有数字视频目标移除取证算法的伪造帧识别准确率低的问题，本文提出了一种基于双通道卷积神经网络的视频目标移除取证算法。 In order to deal with the problem that the existing video object removal forensics methods had low recognition accuracy of the forged frame, a video forensics algorithm based on two-channel convolutional neural network was proposed in this paper.
38832 该算法利用双通道结构，分别提取视频绝对帧差图像的RGB特征和噪声特征，并利用双线性池化对二者进行特征融合，而后通过分类层输出视频帧的分类结果，从而有效地识别经过篡改的视频帧。 This method used a two-channel structure to extract the RGB feature and noise feature of the absolute frame difference image respectively, and the bilinear pooling was utilized to perform feature fusion on the two features. The classification result of the video frames was output through the classification layer, and this method could then identify the forged frame effectively.
38833 其中，RGB通道能够发现绝对帧差图像中不自然的篡改边界和对比度，噪声通道能够发现原始区域和篡改区域之间噪声的不一致性。 The RGB channel was able to find the unnatural tampering boundary and contrast in the absolute frame difference image, and the noise channel was able to find the inconsistency of noise between the original area and the tampered.
38834 此外，算法在网络前端增加了预处理层来放大篡改视频帧的伪造痕迹。 In addition, the algorithm added a preprocessing layer to amplify the forged traces of tampered video frame.
38835 实验结果显示，所提算法有效地提高了伪造视频帧的识别准确率，且相对于传统的单通道网络结构，双通道特征融合的方式取得了更好的检测性能。 The experimental results revealed that the proposed detection algorithm effectively improves the recognition accuracy of forged video frame, and compared with the traditional single-channel network structure, the pattern of two-channel feature fusion achieved better detection performance.