针对水下检测中激光条纹中心难以准确提取的问题，提出了一种适用于水下核燃料棒反光表面的条纹自适应中心提取方法。根据检测环境中存在水体散射及物体表面高反光的特点，去除水下噪点、反光噪点，实现激光条纹分割提取；充分利用BP神经网络曲线拟合及由光条几何信息生成的自适应卷积模板，实现反光区域的轮廓与灰度分布修正，使光条截面灰度分布符合高斯分布；经灰度重心法在光条截面方向实现激光条纹中心的亚像素精度定位与提取。实验结果表明：该方法有效地解决了被测物体反光表面光条中心线不连续、噪点多的问题，点云三维重建误差在0.2 mm以内，保证了条纹中心提取的精度与稳定性，满足核燃料棒水下检测工程要求。

In order to achieve accurate extraction of the center of laser stripes during the underwater detection of nuclear fuel rods, an self-adaptive stripes center extraction method for reflective surface of underwater nuclear fuel rods was proposed. According to characteristics of water scattering and object surface high reflections in detection environment, the underwater noise points and reflective noise points were removed to realize segmentation and extraction of laser stripes; the curve fitting of BP neural network and adaptive convolution template generated from light bar geometry information were utilized to realize contour and gray distribution correction of reflective region, so that the gray distribution of light bar section conformed to the Gaussian distribution; the subpixel precision location and extraction of laser stripes center were realized in light bar section direction by gray centroid method. The experimental results show that this method can effectively solve the problems of discontinuous center line and many noise points of reflective surface light bar of measured object. The 3D reconstruction error of point cloud is within 0.2 mm, which ensures accuracy and stability of stripes center extraction and meets the engineering requirements of underwater detection of nuclear fuel rods.