Adaptive Recognition of Aircraft Cable Brackets Based on Improved Mask R-CNN and Synthetic Dataset

In the field of aircraft assembly, the realization of automatic assembly inspection of aircraft parts is essential to improve aircraft assembly efficiency. However, how to identify each part accurately in a complex assembly scene is still a thorny issue. What's more, deep learning algorithms require large training datasets to converge. In this paper, the problems that are met in the recognition of aircraft cable brackets are analyzed in detail, and a region-based convolutional neural network is optimized by comparing different feature extractors and improving Feature Pyramid Network for small brackets. In addition, a simulation platform is developed based on the Unreal Engine to automatically generate synthetic realistic training datasets. Finally, take the task of inspecting cable brackets in the C919 aircraft as an example, experimental results show that the mAP@50 of the optimized model trained with the synthetic dataset is up to 96.3%.