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.