基于激光图像特征提取的危险目标越界识别研究

Research on dangerous target crossing recognition based onlaser image feature extraction

为了提升危险目标越界激光识别准确性和效率,提出基于激光图像特征提取的危险目标越界识别研究。利用非线性直接变换方法完成激光点云反射图像转换的尺度不变特征点匹配;设计反锐化双掩模法图像增强结构,优化增强处理图像;对点云反射特征点展开沃尔什变换与融合,利用BP神经网络结构,识别越界的危险目标。实验结果表明,所提方法应用后图像特征细节信息得到增强,不存在局部曝光问题,对危险目标越界的错检率和漏检率最低,且具有较高的识别精度和识别效率。

In order to improve the accuracy and efficiency of dangerous target boundary crossing laser recognition,a research on dangerous target boundary crossing recognition based on laser image feature extraction is proposed.Using nonlinear direct transformation method to achieve scale invariant feature point matching in laser point cloud reflection image conversion;Design an anti sharpening dual mask image enhancement structure to optimize image enhancement processing;Perform Walsh transform and fusion on the reflection feature points of the point cloud,and use the BP neural network structure to identify dangerous targets that are out of bounds.The experimental results show that the proposed method enhances the detailed information of image features after application,does not have local exposure issues,has the lowest false detection rate and missed detection rate for dangerous targets exceeding the boundary,and has high recognition accuracy and efficiency.