基于拼接算法的炮膛疵病自动识别技术

针对当前采用目测识别方法进行炮膛疵病检测时存在的识别速度慢、精度低、人为影响因素大等缺点，提出了一种基于拼接算法的炮膛疵病检测方法。该方法采用Harris角点检测算法进行特征点提取，采用归一化互相关法进行特征点初匹配，采用马氏距离提纯算法消除误匹配，采用改进的8参数透视变换优化估计算法和基于边界保持的函数加权平滑融合算法进行炮膛图像融合，并选取图像面积、短长径之比为特征参数完成疵病的自动识别。实验结果表明：该算法对炮膛图像能较好地进行拼接，而且数值化的疵病识别方法相比传统目测识别方法也更加快速、准确，可为部队和相关工程人员进行身管检测提供有益参考。

Automatic Recognition Techniques of Artillery Bore Flaw Based on Jointing Algorithm

In allusion to the shortcomings of slow detection speed, low accuracy and great artificial factors existed in the current artillery bore flaw recognition by visual detection, a method of artillery bore flaw recognition based on jointing algorithm is proposed. The method extracts feature points by using Harris corner detection algorithm, matches feature point by using the normalized cross correlation method, elimi- nates false match by using Ma distance purification algorithm, fuses the bore image by using improved 8- parameter perspective optimization estimation algorithm and boundary weighted smoothing fusion algo- rithm, and takes image area and the ratio of short and long diameters as the characteristic parameters to complete automatic identification of defects. The experimental results show that the algorithm is good for bore image jointing, and the numerical flaw image recognition is more rapid and precise than the tradi- tional visual detection, which can provide a useful reference for army and engineering staff.