基于GSV-YOLO的飞机起落架缺陷检测方法研究  

Research on defect detection method of aircraft landing gearbased on GSV-YOLO

作　　者：李博 许子威 钟飞[1] 陈义华 Li Bo;Xu Ziwei;Zhong Fei;Chen Yihua(School of Mechanical Engineering,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学机械工程学院,武汉430068

出　　处：《电子测量技术》2025年第5期175-183,共9页Electronic Measurement Technology

基　　金：湖北省重点研发计划项目(2023BAB088)资助。

摘　　要：飞机起落架缺陷检测对于确保飞行安全具有重要意义。针对现有目标检测方法存在的精度不足、模型参数量大等问题,提出了一种名为GSV-YOLO的飞机起落架缺陷检测方法,该方法基于YOLOv7-tiny并对其进行改进。首先,采用Ghost卷积替代YOLOv7-tiny模型中的标准卷积,实现模型轻量化并减少参数量;其次,引入深度可分离自注意力模块(SepViT),增强模型对全局信息的提取能力,减少小目标样本的误检和漏检;设计轻量级检测头以强化模型的分类和定位能力;最后,利用基于Wise-IoU损失的动态非单调聚焦机制对普通质量锚框进行聚焦,进一步提升检测性能。实验结果表明,GSV-YOLO在飞机起落架缺陷数据集上平均检测精度达到80.4%,相较于原模型提升了4.9%,且模型参数量减少了10.6%,同时改善了模型规模和检测精度。将该方法应用于工业环境,显著提高了检测效率,具有极大应用价值。Aircraft landing gear defect detection is of great significance to ensure flight safety.Aiming at the problems of insufficient accuracy and large number of parameters in the existing target detection methods,an aircraft landing gear defect detection method named GSV-YOLO is proposed,which is based on YOLOv7-tiny and improved.Firstly,Ghost convolution is used to replace the standard convolution in the YOLOv7-tiny model to realize the model lightweight and reduce the number of parameters;secondly,a depth-separable self-attention module(SepViT)is introduced to enhance the model′s ability of extracting the global information and to reduce the misdetection and underdetection of small target samples;a lightweight detector head is designed to strengthen the model′s classification and localization ability;finally,the dynamic non-monotonic focusing mechanism based on Wise-IoU loss to focus on common quality anchor frames to further enhance the detection performance.The experimental results show that GSV-YOLO achieves an average detection accuracy of 80.4%on the aircraft landing gear defect dataset,which is 4.9%higher compared to the original model,and the amount of model parameters is reduced by 10.6%,which improves both the model scale and detection accuracy.Applying this method to industrial environments significantly improves the detection efficiency and has great application value.

关 键 词：飞机起落架 缺陷检测 YOLOv7-tiny Ghost卷积 深度学习 

分 类 号：TN911.73[电子电信—通信与信息系统]