基于多特征的洞库类目标识别方法

针对洞库类目标自动识别研究较少、识别率较低、识别方法成本较高等问题,设计了基于多种特征的洞库类目标识别算法.探讨了典型洞库类目标的模型并总结其主要特征;其次利用HOG特征对输入图像进行初步筛选,筛选出包含有洞库类目标的图像;然后基于洞库类目标的灰度特征提出了一种图像局部自适应阈值生成算法Wiblack提取图像中的疑似目标;最后搭建了洞库类目标的数学模型,并提出了基于形状相似度的目标判别算法,采用圆形相似度与椭圆形状相似度二次相似度判别方法,最终得出识别结果并描述目标轮廓,完成目标识别.实验结果表明该方法在洞库类目标的识别应用中有效可行,基于本文实验数据的识别准确度为92.6%.

Cave Target Recognition Method Based on Multiple Characteristics

In order to solve the problems of automatic identification of cave target, a target recognition algorithm was designed based on various features. Firstly, discussing the model of typical cave target, summarizing its main features, and filtering the input images according to HOG feature, the images of the cave target were classified. Then a self-adaptive threshold algorithm Wiblack was proposed based on the gray feature of the cave target to extract the suspected target in the image. Finally, a mathematical model of the cave target was designed, and a target discrimination algorithm was developed based on shape similarity. The target discrimination and the target profile describing were completed with circular and ellipse similarity methods. The experimental results show that the proposed algorithm possesses better recognition accuracy, higher adaptability and robustness.