ID 原文 译文
40756 传统的利用传感器模式噪声的相机源识别方法，自动化程度低。 The traditional camera source identification method using sensor pattern noise has a low degree of automation.
40757 直接使用深度学习方法进行识别，提取特征阶段又更倾向于学习到图像内容有关的特征，而不是相机"指纹"特征。 The deep learning method is directly used for identification, and the feature extraction stage is more inclined to learn the features related to the image content, rather than the camera "fingerprint" feature.
40758 为了解决上述问题，本文设计了一种基于孪生网络的数字图像相机源识别架构，两个网络分支共享权重。 In order to solve the above problems, this paper designs a digital image camera source identification architecture based on the Siamese network, and the two network branches share the weight.
40759 采用加入注意力机制的深度残差网络进行特征提取，抑制图像内容等因素对整个相机源识别任务的影响。 A deep residual network with an attention mechanism is used for feature extraction to suppress the influence of image content on the entire camera source identification task.
40760 对比实验结果表明，本文提出的方法在一定程度上提高了相机源识别的准确率。 The comparative experiment results show that the method proposed in this paper improves the accuracy of camera source identification to a certain extent.
40761 随着识别率和实时性的提高，卷积神经网络目标检测算法的计算复杂度和内存需求急剧增加，难以应用在小尺寸和低功耗的嵌入式平台上。 With the improvement of recognition rate and real-time performance, the computational complexity and memory requirements of convolutional neural network target detection algorithm increase sharply, which makes it difficult to be applied to embedded platform with small size and low power consumption.
40762 本文在分析现有目标检测神经网络模型结构的基础上，根据FPGA高实时性、低功耗以及并行处理的特点，提出了一种在FPGA上高速运算的神经网络模型规整化方法。 In this paper, based on the analysis of the existing neural network model structure of target detection, according to the characteristics of high real-time performance, low power consumption and parallel processing of FPGA, a neural network model normalization method based on high speed operation on FPGA is proposed.
40763 在此方法指导下设计改进了一款目标检测神经网络模型结构，包括删除LRN层、Scale层的融合和替换Leaky-ReLU为ReLU。 Under the guidance of this method, a target detection neural network model structure is designed and implemented, including removing the LRN layer, fusion of Scale layer and replacing Leaky-ReLU with ReLU.
40764 通过在voc2007数据集上的对比实验验证了算法结构的有效性， The effectiveness of the proposed algorithm structure is verified by comparative experiments on VOC2007 dataset.
40765 在PC上其速度相比传统YOLO-V1算法提升了11.5%。 Compared with traditional YOLO-V1 algorithm, the speed of the proposed algorithm on PC is improved by 11.5%.