Aiming at the problem that the traditional feature point detection algorithm needs to develop the detection mechanism manually and the weak generalization ability of featurepoint detection network based on deep learning.The Resinv-Unet neural network with pixel-level feature point detection capability is designed and implemented by introducing gray-value invariant and residual.The data source for training neural networks is constructed by self-labeling on the basis of the real scene image data set.The experimental results show that compared with the existing feature point detection algorithms and feature point detection net works,Resinv-Unet has stronger generalization ability and robustness in real scene images,and has better performance in terms of average accuracy,accuracy and recall,with average accuracy of 0.7155,accuracy of 0.7762,and recall 0.7137.