基于U-net的被动式太赫兹安检危险品分割方法

针对被动式太赫兹安检系统检测图像识别危险物难度较大、精确度不高的问题，提出一种基于U-net的被动式太赫兹安检危险物分割算法。通过构建危险品的局部结构差异性假设和局部亮度差异性假设定位太赫兹安检图像中危险品可能存在的感兴趣区域(ROI)，并选择拥有少量特征通道与神经元的浅层卷积网络针对ROI做图像超分辨处理，最后将图像输入U-net网络,得到质量高、轮廓清晰的危险品分割图像。通过实验证实了本文方法相比传统分割算法准确性有明显提高，有助于提高被动式太赫兹安检系统的危险品识别率。

Hazard segmentation algorithm of passive terahertz security check based on U-net

A dangerous item segmentation algorithm for passive terahertz imaging security inspection is proposed in response to the difficulty and low precision of dangerous item recognition in the passive terahertz imaging. First of all, the hypothesis of the dangerous item local structural difference and the hypothesis of the local luminance difference are made to locate the Region Of Interest(ROI) where dangerous items might exist in terahertz images. Meanwhile, the shallow convolutional network containing a few feature channels and nerve cells is chosen for super-resolution processing of images in ROI regions. The images are input into the U-net to obtain high-quality and clearly-outlined partitioned images of dangerous items. Finally, an experiment is conducted to verify the improvement of the detection accuracy of the proposed algorithm in comparison with traditional partitioning algorithms. This is conducive to improving the recognition rate of dangerous items by the passive terahertz imaging security inspection system.