基于机器视觉的飞机故障检查系统

Aircraft Fault Inspection System Based on Machine Vision

针对当前飞机维修检查工作以人工目视检查为主、效率低且存在人为因素影响的情况,设计一套基于图像识别与机器深度学习的飞机部件表面无损检测系统。收集并整理了某航空公司一线飞机维修员拍摄的飞机机身及发动机部件图片,对图片集进行预处理,包括通道提取、Sobel滤波处理及二值化;最后用Blob分析对处理后的图像进行特征提取与系统分析。系统运行速度快、准确率高且可连续自动识别图像。利用机器视觉技术对飞机部件表面进行无损检测不仅可以提高生产效率,同时可以去除人为因素对航空器飞行安全的影响,使得飞机的飞行安全得到进一步提升。实践证明,该系统性能稳定可靠,具有极高的推广应用价值。

Aiming at the current aircraft maintenance inspection with manual visual inspection,low efficiency and human factors,a nondestructive inspection system of aircraft parts surface based on image recognition and machine deep learning was designed.The images of aircraft fuselage and engine parts taken by the first-line aircraft maintenance personnel of an airline company were collected and sorted out.The image set was preprocessed,including channel extraction,Sobel filtering and binarization.Finally,Blob analysis was used to make the features extraction and analyze for the processed images.The system runs fast,has high accuracy and can recognize the image continuously automatically.Using machine vision technology to carry out nondestructive testing on the surface of aircraft parts can not only improve the production efficiency,but also remove the influence of human factors on aircraft flight safety,so as to further improve the flight safety of aircraft.The practice shows that the system is stable and reliable,and has high application value.