基于贝叶斯决策理论的爆炸物识别方法

针对安全检查领域爆炸物识别问题，以辐射数据的特征提取和识别为核心，将双能量X射线透射技术与低能前散射和背散射技术相结合，实验得到双能量透射、低能前散射及低能背散射图像灰度级并计算与物质有效原子序数相关的特征值R和与密度相关的特征值L，给出基于最小错误概率的贝叶斯决策理论的判别函数、决策面方程以及分类判别规则.实验验证贝叶斯决策理论的分类判别规则的识别正确率达到90%，将双能量X射线透射技术与低能散射技术相结合找到物质识别更有效的方法，进而全面提高X射线探测能力，这是对固体爆炸品识别的重要贡献.

An Explosives Identification Method Based on the Bayesian Decision Theory

For explosives recognition problem in safety inspection field, dual-energy X-ray transmission technology, low-energy forward scattering and back scattering technology were combined to get the gray-levels of dual-energy transmission, low-energy forward scattering and back scattering images taking the feature extraction and recognition of the radiation data as the core. The eigenvalue R associated with effective atomic number and the eigenvalue L associated with density were obtained. Based on the least mistake probability, the eigenvalue R and L were synthesized to get the discriminate, decision-making plane and distinguish rule. The experiments validated that the correct rate of the discrimination rules by the Bayesian decision theory is up to 90%, and it is a more effective method for solid explosives identification that improves the detection capability of X-ray comprehensively. This may significantly contribute to recognition of solid explosives.