基于改进U-Net的刑侦红外手印目标提取

红外图像技术在刑侦领域的应用越来越广泛。通过案发现场遗留的热痕迹可为案件搜集更多的证据。但是红外图像往往存在轮廓不清晰、提取效果模糊等问题。针对这些问题提出一种基于改进U-Net的图像目标提取方法。网络结构采用三层的上采样和下采样：上采样采用双立方插值，下采样采用步长为2的3×3卷积。通过跳跃连接将浅层特征和深层语义信息融合，并引入Dropout和Batch Normalization结构使得网络更快、更好地收敛。以刑侦红外手印为研究对象，通过实验对比改进U-Net、Sobel算子、分水岭、最大熵、Otsu等算法的图像目标提取效果。结果表明，本文搭建的U-Net网络能够更完整、更有效地提取红外手印的轮廓信息，在红外手印的提取上取得了较好的效果。

Target Extraction of Infrared Fingerprint in Criminal Investigation Based on Improved U-Net

Infrared image technology is applied more and more widely in the field of criminal investigation. Heat traces left at the crime scene can be used to gather more evidence for the case. However, there are always some problems such as unclear contour and fuzzy extraction effect existing in infrared images. An image object extraction method based on improved U-Net to solve these problems is proposed in this paper. Three layers of up-sampling and down-sampling are adopted in the network structure. Double-cubic interpolation is used for up-sampling, and 3×3 convolution with step size of 2 is used for down-sampling. The shallow feature and deep semantic information are combined by skip connection. The introduction of Dropout and Batch Normalization structures makes the network converge faster and better. Taking infrared fingerprint of criminal investigation as the research object, the image object extraction effects of U-Net, Sobel operator, watershed, maximum entropy and Otsu are improved by comparing experiments. The results show that the U-Net network built in this paper can extract the contour information of infrared fingermarks more completely and effectively, and good results are achieved in the extraction of infrared fingermarks.