In order to solve the problems of unstable number of feature points, false detection, missed detection, poor size invariance, and low stitching accuracy under the traditional ORB algorithm in the leaf shape image stitching of aero engines, this paper carries out experimental research on leaf shape image stitching and proposes an improved corner detection ORB-CNN algorithm combined with a convolutional neural network. The idea of the algorithm is as follows: The improved corner detection algorithm is applied to realise corner extraction at different scales under the construction of the image pyramid. In the feature point detection stage, the convolutional neural network(CNN) is used to replace the FAST-n detection established by the 16-pixel ring in the FAST algorithm, and the convolutional layer is added to the CNN according to the number of pixels in the domain, that is, the design convolutional kernel is added to extract features related to FAST-n detection in the image. The BRIEF method was used to obtain the descriptor of the detected feature points, and the Hamming distance was calculated, so as to achieve accurate matching of the feature points. Experimental results show that compared with the traditional ORB algorithm and SIFT algorithm, the uniformity of feature point extraction of the improved algorithm is increased by 18.83% and 33.36%, respectively. In the illumination change experiment, the accuracy of feature point matching of the improved algorithm under strong light and dark light conditions is improved by 16.63% and 19.04%, respectively. Compared with the original ORB algorithm and SIFT algorithm, the feature point offset and matching accuracy are increased by 66.95%, 64.26%, 12.63% and 6.62%, respectively. This method effectively overcomes the limitations of the traditional ORB algorithm in terms of size invariance, and also retains the advantages of the ORB algorithm in terms of registration speed and quality, significantly improving detection performance and robustness in complex environments, and laying a foundation for the non-contact measurement of the blade gap of aero engines.