In order to solve the problem of low recognition accuracy of aero⁃engine lockwire caused by factors of the complex background, uneven illumination and small percentage of the target region, this paper proposes an improved mask region⁃based convolutional neural network (Mask R⁃CNN) model for lockwire instance segmentation. Firstly, the gamma corrections of R, G and B channels with different degrees were carried out to transform the lockwire image into pseudo⁃color image and enhance the contrast. Then, the dynamic snake⁃shaped convolution was incorporated into Resnet, the backbone network of Mask R⁃CNN, to make the network to adaptively focus on the slender and curved local structure during feature extraction. Then, based on the geometric features of the fuse's slender curve, dynamic snake convolution was integrated into the backbone network Resnet of Mask R⁃CNN, allowing the network to adaptively focus on the local structure of the slender curve during feature extraction. Finally, the CBAM attention mechanism was introduced in the feature fusion phase to retain the shallow features of small target, so as to improve the perception ability of the network on small target. The experimental results showed that the A_AP50 of the improved module mask reached 82.54%, which was improved by 5.83% compared to basic mode. This study provides strong support for digital and intelligent detection of aero⁃engine lockwire.