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

作　　者：Xiaoqi YANG Xingyue LIU Qian WU Guojun WEN Shuang MEI Guanglan LIAO Tielin SHI 

机构地区：[1]School of Mechanical Engineering and Electronic Information,China University of Geosciences,Wuhan 430074,China [2]State Key Laboratory of Digital Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Frontiers of Mechanical Engineering》2024年第3期77-92,共16页机械工程前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.52305623);the Natural Science Foundation of Hubei Province,China(Grant No.2022CFB589);the Natural Science Foundation of Chongqing,China(Grant No.CSTB2023NSCQ-MSX0636).

摘　　要：The quality of the exposed avionics solder joints has a significant impact on the stable operation of the inorbit 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 highdensity 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 VMMAOYOLO 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.

关 键 词：defect detection of solder joints VMMAO-YOLO ultra-lightweight and high-performance multiscale feature extraction VMC and MAA modules OS-GD 

分 类 号：TB302[一般工业技术—材料科学与工程]