VMMAO-YOLO:an ultra-lightweight and scale-aware detector for real-time defect detection of avionics thermistor wire solder joints

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外文摘要：The quality of the exposed avionics solder joints has a significant impact on the stable operation of the in-orbit spacecrafts.Nevertheless,the previously reported inspection methods for multi-scale solder joint defects generally suffer low accuracy and slow detection speed.Herein,a novel real-time detector VMMAO-YOLO is demonstrated based on variable multi-scale concurrency and multi-depth aggregation network(VMMANet)backbone and"one-stop"global information gather-distribute(OS-GD)module.Combined with infrared thermography technology,it can achieve fast and high-precision detection of both internal and external solder joint defects.Specifically,VMMANet is designed for efficient multi-scale feature extraction,which mainly comprises variable multi-scale feature concurrency(VMC)and multi-depth feature aggregation-alignment(MAA)modules.VMC can extract multi-scale features via multiple fix-sized and deformable convolutions,while MAA can aggregate and align multi-depth features on the same order for feature inference.This allows the low-level features with more spatial details to be transmitted in depth-wise,enabling the deeper network to selectively utilize the preceding inference information.The VMMANet replaces inefficient high-density deep convolution by increasing the width of intermediate feature levels,leading to a salient decline in parameters.The OS-GD is developed for efficacious feature extraction,aggregation and distribution,further enhancing the global information gather and deployment capability of the network.On a self-made solder joint image data set,the VMMAO-YOLO achieves a mean average precision mAP@0.5 of 91.6% ,surpassing all the mainstream YOLO-series models.Moreover,the VMMAO-YOLO has a body size of merely 19.3 MB and a detection speed up to 119 frame per second,far superior to the prevalent YOLO-series detectors.

外文关键词：

defect detection of solder jointsVMMAO-YOLOultra-lightweight and high-performancemulti-scale feature extractionVMC and MAA modulesOS-GD

作者：

Xiaoqi YANG、Xingyue LIU、Qian WU、Guojun WEN、Shuang MEI、Guanglan LIAO、Tielin SHI

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作者单位：

School of Mechanical Engineering and Electronic Information,China University of Geosciences,Wuhan 430074,China

State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China

基金：

National Natural Science Foundation of ChinaNatural Science Foundation of Hubei Province,ChinaNatural Science Foundation of Chongqing,China

项目编号：

523056232022CFB589CSTB2023NSCQ-MSX0636

出版年：

2024

DOI：

10.1007/s11465-024-0793-3

机械工程前沿

高等教育出版社

机械工程前沿

影响因子：0.118

ISSN：2095-0233

年,卷(期)：2024.19(3)