融合LoG特征的凸焊螺母检测算法

doi:10.3778/j.issn.1002-8331.2302-0135

摘要/Abstract

摘要： 针对目前汽车曲面零部件的紧固连接中常用的凸焊工艺中出现凸焊螺母的漏焊、错焊，以及主要依赖人工目测的低效检测方法等问题，提出了一种基于Faster-RCNN的凸焊螺母检测算法。以Faster-RCNN作为基础模型，针对模型在不同角度下螺母特征各异且难以提取的问题，提出提取LoG特征和原图像自适应融合的方法，以增强模型对螺母特征的提取能力；引入特征金字塔（feature pyramid network，FPN）解决小目标难以被精确检测的问题；为了提升网络在复杂背景中的检测鲁棒性，在FPN中嵌入坐标注意力机制来提升网络对重点目标的关注；设计损失函数，提升训练效果，增强回归框中心点的回归精确度。实验结果表明，所提算法相比原算法，在IoU=0.75时凸焊螺母的检测精确率上升了8.65个百分点，达到90.11%，召回率上升了5.87个百分点，达到79.23%，相比原算法具有明显改善。

关键词: 目标检测, 特征金字塔网络（FPN）, 坐标注意力, LoG特征, 区域建议网络（RPN）

Abstract: In order to solve the problems of missing welding and miswelding of convex welding nuts in the common convex welding process for the connection of automobile curved parts, and the low efficiency detection method mainly relying on manual visual inspection, a visual target detection algorithm for welding nuts based on Faster-RCNN is proposed. Firstly, taking Faster-RCNN as the basic model, aiming at the problem that it is difficult to extract nut features from different angles, a method of adaptive fusion of artificial features and original image is proposed to enhance the ability of the model to extract nut features, and feature pyramid network (FPN) is introduced to solve the problem that small targets are difficult to be accurately detected. Then, in order to improve the detection robustness of the network in the complex background, the coordinate attention mechanism is embedded in the FPN to improve the attention of the network to the key targets. Finally, the loss function is designed to improve the training effect and enhance the regression accuracy of the central point of the regression box. The experimental results show that compared with the original algorithm, the average precision (AP) of convex welding nuts at IoU=0.75 increases 8.65?percentage points to 90.11% and the recall increases 5.87?percentage points to 79.23%, which is a significant improvement compared with the original algorithm.

Key words: object detection, feature pyramid network (FPN), coordinate attention, LoG feature, region proposal network (RPN)

罗柏槐, 李扬, 林熙烨, 周梓斌. 融合LoG特征的凸焊螺母检测算法[J]. 计算机工程与应用, 2024, 60(10): 332-340.

LUO Baihuai, LI Yang, LIN Xiye, ZHOU Zibin. Weld Nut Detection Algorithm Based on LoG Features Fusion[J]. Computer Engineering and Applications, 2024, 60(10): 332-340.

参考文献

[1] 黄立新. 基于深度学习的钣金组焊缺陷在线检测系统研究[D]. 成都：西华大学, 2022.
HUANG L X. Research on on-line defect detection system of sheet metal assembly welding based on deep learning[D]. Chengdu: Xihua University, 2022.
[2] 黄桦, 滕海渤, 刘义法, 等.机器视觉技术在汽车制造行业中的应用研究[J].汽车工艺与材料, 2022(6): 8-15.
HUANG H, TENG H B, LIU Y F, et al. Research on application of machine vision technologies in automobile manufacturing industry[J]. Automobile Technology & Material, 2022(6): 8-15.
[3] 赵朗月, 吴一全.基于机器视觉的表面缺陷检测方法研究进展[J].仪器仪表学报, 2022, 43(1): 198-219.
ZHAO L Y, WU Y Q. Research progress of surface defect detection methods based on machine vision[J]. Chinese Journal of Scientific Instrument, 2022, 43(1): 198-219.
[4] 肖庆阳, 方建儒, 张效民, 等.基于机器视觉的多螺孔检测系统[J].组合机床与自动化加工技术, 2021(1): 126-127.
XIAO Q Y, FANG J R, ZHANG X M, et al. Multi-threaded hole detection system based on machine vision[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2021(1): 126-127.
[5] 马晓锋, 王中任.基于引导滤波与神经网络算法的螺纹孔检测方法[J].制造技术与机床, 2022(1): 165-170.
MA X F, WANG Z R. Threaded hole detection method based on guided filtering and neural network algorithm[J]. Manufacturing Technology & Machine Tool, 2022(1): 165-170.
[6] 郭术义, 杨赛, 张鹏博.机器视觉在机械密封圈在线分拣中的应用[J].国外电子测量技术, 2021, 40(11): 103-110.
GUO S Y, YANG S, ZHANG P B. Application of machine vision in on-line sorting of mechanical sealing rings[J]. Foreign Electronic Measurement Technology, 2021, 40(11): 103-110.
[7] 樊绍胜, 杨迪, 邹德华, 等.输电线路螺栓紧固带电作业机器人的视觉搜索、识别与定位方法[J].电子测量与仪器学报, 2017, 31(9): 1514-1523.
FAN S S, YANG D, ZOU D H, et al. Vision-based tracing, recognition and positioning strategy for bolt tightening live working robot on power transmission line[J]. Journal of Electronic Measurement and Instrumentation, 2017, 31(9): 1514-1523.
[8] 王伏林, 冯显东, 冷细元, 等.基于深度学习与支持向量机的废旧产品锈蚀螺栓可拆卸性判别方法[J].现代制造工程, 2022(11): 122-129.
WANG F L, FENG X D, LENG X Y, et al. Method for dismountability judgement of rusted bolts of waste products based on deep learning and support vector machine[J]. Modern Manufacturing Engineering, 2022(11): 122-129.
[9] 杨桂华, 刘志毅, 王晓文.基于机器视觉多目标工件分类识别和定位研究[J].机床与液压, 2021, 49(11): 82-86.
YANG G H, LIU Z Y, WANG X W. Research on classification, recognition and localization of multi-objective workpiece based on machine vision[J]. Machine Tool & Hydraulics, 2021, 49(11): 82-86.
[10] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[11] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[12] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6517-6525.
[13] REDMON J, FARHADI A. YOLOv3: an incremental improvement[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018: 2767-2773.
[14] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[C]//Proceedings of the 2020 IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[15] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1440-1448.
[16] REN S, HE K AND GIRSHICK R. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[17] HE K, GKIOXARI G, PIOTR D, et al. Mask R-CNN[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, 2017: 2980-2988.
[18] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2117-2125.
[19] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[20] 肖扬, 周军.图像边缘检测综述[J].计算机工程与应用, 2023, 59(5): 40-54.
XIAO Y, ZHOU J. Overview of image edge detection[J]. Computer Engineering and Applications, 2023, 59(5): 40-54.
[21] 包从望, 胡才梦, 张彩红，等.基于改进Canny算子的齿轮缺陷检测边缘检测算法[J].组合机床与自动化加工技术, 2023(1): 83-86.
BAO C W, HU C M, ZHANG C H, et al. Edge detection algorithm of gear defect detection based on improved Canny operator[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2023(1): 83-86.
[22] 杜伟, 何毅斌, 吴林慧，等.融合改进形态学和LOG算子的齿轮边缘检测[J].武汉工程大学学报, 2021, 43(6): 675-680.
DU W, HE Y B, WU L H, et al. Gear edge detection by fusion of improved morphology and LOG operator[J]. Journal of Wuhan Institute of Technology, 2021, 43(6): 675-680.
[23] 贺强, 晏立.基于LOG和Canny算子的边缘检测算法[J].计算机工程, 2011, 37(3): 210-212.
HE Q, YAN L. Algorithm of edge detection based on LOG and Canny operator[J]. Computer Engineering, 2011, 37(3): 210-212.
[24] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.