中文摘要：

针对纹身图像的特点和卷积神经网络（CNN）在全连接层对图像特征抽取能力的不足问题，提出一种三通道的卷积神经网络纹身图像检测算法，并进行了三方面的改进工作。首先，针对纹身图像的特点改进图像预处理方案；其次，设计了一个基于三通道全连接层的卷积神经网络进行特征提取，并对特征建立索引，有效地提高了网络对不同尺度下空间信息的提取能力，实现了对纹身图像的高效检测；最后，通过两个数据集验证了算法的泛化能力。实验结果表明，对NIST数据集所提预处理方案比Alex方案有总正确率提高0.17个百分点，纹身图像正确率提高0.29个百分点。在所提预处理方案下，提出的算法在标准的NIST纹身图像集上具有明显的优势，正确率从NIST公布的最优值96.3%提高到99.1%，提高了2.8个百分点；相对于传统的CNN算法，正确率从98.8%提高到99.1%，提高了0.3个百分点。在Flickr数据集上也有相应的性能提升。

英文摘要：

According to the characteristics of tattoo images and the insufficient ability of the Convolutional Neural Network （CNN） to extract the image features in the full connection layer, a tattoo image detection algorithm based on three-channel CNN was proposed, and three aspects of improvement work were carried out. Firstly, the image preprocessing scheme was improved for the characteristics of tattoo images. Secondly, a CNN based on three-channel fully connected layer was designed to extracted and index the features. The spatial information extraction ability of different scales was enhanced effectively, and the efficient detection of tattoo images was realized. Finally, the generalization ability of the algorithm was verified by two data sets. The experimental results on the NIST data set show that the proposed preprocessing scheme has a 0.17 percentage points increase of total correct rate and a 0.29 percentage points increase of correct rate for tattoo images than Alex scheme. Under the proposed preprocessing scheme, the proposed algorithm has obvious advantages on the standard NIST tattoo image set. The correct rate of the proposed algorithm reaches 99.1%, which is higher than 96.3%, the optimal value published by NIST; and 98.8%, obtained by traditional CNN algorithm. There is also a performance improvement on the Flickr data set.