Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (1): 234-240.doi: 10.13229/j.cnki.jdxbgxb20211301

Previous Articles    

Asymmetric defect detection method of small sample image based on meta learning

Peng-qi⁃zi HUANG1(),Xiao-jun DUAN1,Wen-wei HUANG2,Liang YAN1   

  1. 1.College of Arts and Sciences,National University of Defense Technology,Changsha 410072,China
    2.College of Military Basic Education,National University of Defense Technology,Changsha 410072,China
  • Received:2021-11-29 Online:2023-01-01 Published:2023-07-23

Abstract:

Taking the accurate detection of asymmetric defects in small sample images as the research core, a small sample image asymmetric defect detection method based on meta learning is proposed. The adaptive filtering method of small sample image based on modulation is used to remove the noise of small sample image and optimize the quality of small sample image; Through the defect feature extraction method of filtered small sample image based on boundary detection, the defect feature of filtered small sample image is extracted; The extracted features are used as the detection samples of the small sample image asymmetric defect feature detection method based on improved meta learning to realize the small sample image asymmetric defect detection. The experimental results show that the proposed method has good filtering effect on small sample images. When detecting a variety of asymmetric defects, when the number of defect features of small sample images increases, the minimum value of intersection and union ratio of this method is 0.9, and the value of intersection and union ratio is ideal, which can accurately detect asymmetric defects of small sample images.

Key words: meta learning, small samples, image, asymmetric, defect detection, gradient descent method

CLC Number: 

  • TP391.4

Fig.1

Structure information of meta learning"

Fig.2

Structure information of gradient descent method"

Fig.3

Original drawing of black spot asymmetricdefect"

Fig.4

Original drawing of crack type asymmetricdefect"

Fig.5

Filtering results of black spot asymmetric defect image"

Fig.6

Filtering results of crack type asymmetric defect image"

Fig.7

Test results of black spot asymmetric defects"

Fig.8

Test results of crack type asymmetric defects"

Fig.9

Intersection and combination comparison view"

Fig.10

Intersection union ratio of asymmetric defect detection results"

1 蔡彪, 沈宽, 付金磊,等.基于Mask R-CNN的铸件X射线DR图像缺陷检测研究[J].仪器仪表学报, 2020, 41(3): 61-69.
Cai Biao, Shen Kuan, Fu Jin⁃lei, et al. Research on defect detection of X-ray DR images of casting based on Mask R-CNN[J]. Chinese Journal of Scientific Instrument, 2020, 41(3): 61-69.
2 张红岩, 王永志, 刘庆红.图像识别技术在食品包装缺陷检测中的应用[J].食品与机械, 2020, 36(8): 225-228.
Zhang Hong-yan, Wang Yong-zhi, Liu Qing-hong. Application of image recognition technology in food packaging defect monitoring[J]. Food and Machinery, 2020, 36(8): 225-228.
3 马浩鹏, 朱春媚, 周文辉, 等.基于深度学习的乳液泵缺陷检测算法[J].液晶与显示, 2019, 34(1): 81-89.
Ma Hao-peng, Zhu Chun-mei, Zhou Wen-hui, et al. Defect detection algorithm of lotion pump based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(1): 81-89.
4 王昕钰, 王倩, 程敦诚, 等.基于三级级联架构的接触网定位管开口销缺陷检测[J].仪器仪表学报, 2019, 40(10): 74-83.
Wang Xin-yu, Wang Qian, Chen Dun-cheng, et al. Detection of split pins defect in catenary positioning tube based on three-level cascade architecture[J]. Chinese Journal of Scientific Instrument, 2019, 40(10): 74-83.
5 杨正伟, 谢星宇, 李胤, 等.激光扫描热成像无损检测关键参数影响分析[J].红外与激光工程, 2019, 48(11): 91-101.
Yang Zheng-wei, Xie Xing-yu, Li Yin, et al. Influence analysis of key parameters in laser scanning thermography nondestructive testing[J]. Infrared and Laser Engineering, 2019, 48(11): 91-101.
6 戴铭, 叶木超, 周智恒, 等.基于先验分布活动轮廓模型的纹理缺陷检测[J].控制与决策,2020, 35(5): 1226-1230.
Dai Min, Ye Mu-chao, Zhou Zzhi-heng, et al. Texture defects detection based on prior distribution active contour model[J]. Control and Decision, 2020, 35(5): 1226-1230.
7 于重重, 萨良兵, 马先钦, 等.基于度量学习的小样本零器件表面缺陷检测[J]. 仪器仪表学报, 2020, 41(7): 214-223.
Yu Chong-chong, Liang-bing Sa, Ma Xian-qin,et al. Few-shot parts surface defect detection based on the metric learning[J]. Chinese Journal of Scientific Instrument, 2020, 41(7): 214-223.
8 袁桂霞, 周先春.基于分类和模糊滤波的X光图像椒盐噪声滤除算法[J].计算机应用研究, 2019, 36(1): 299-302.
Yuan Gui-xia, Zhou Xian-chun. Salt and pepper noise filtering algorithm for X-ray images with multi-level classification and fuzzy filtering[J]. Application Research of Computers, 2019, 36(1): 299-302.
9 武昊男, 储成群, 任勇峰, 等.基于FPGA的图像自适应加权均值滤波设计[J].电子技术应用, 2019, 45(3): 32-35, 41.
Wu Hao-nan, Chu Cheng-qun, Ren Yong-feng, et al. Self-adaption image weighted mean filter design based on FPGA[J]. Journal of Electronic Technology Application, 2019, 45(3): 32-35, 41.
10 方斌, 周辉奎, 陈家益, 等.去除脉冲噪声的快速开关中值图像滤波[J].控制工程, 2021, 28(6): 1243-1249.
Fang Bin, Zhou Hui-kui, Chen Jia-yi, et al. A Fast switching median image filter for impulse noise removal[J]. Control Engineering of China, 2021, 28(6): 1243-1249.
11 罗凯鑫, 吴美平, 范颖.基于最大熵方法的鲁棒自适应滤波及其应用[J]. 系统工程与电子技术, 2020, 42(3): 667-673.
Luo Kai-xin, Wu Mei-ping, Fan Yin. Robust adaptive filtering based on maximum entropy method and its application[J]. Journal of Systems Engineering and Electronics, 2020, 42(3): 667-673.
12 吴一全, 邹宇, 刘忠林.基于Franklin矩的亚像素级图像边缘检测算法[J].仪器仪表学报, 2019, 40(5): 221-229.
Wu Yi-quan, Zhou Yu, Liu Zhong-lin. Sub-pixel level image edge detection algorithm based on Franklin moments[J], Journal of Scientific Instrument, 2019, 40(5): 221-229.
13 罗元, 王薄宇, 陈旭.基于深度学习的目标检测技术的研究综述[J]. 半导体光电, 2020, 41(1): 1-10.
Luo Yuan, Wang Bo-yu, Chen Xu. Research progresses of target detection technology based on deep learning[J]. Semiconductor Optoelectronics, 2020, 41(1): 1-10.
14 罗会兰, 陈鸿坤.基于深度学习的目标检测研究综述[J].电子学报, 2020, 48(6): 1230-1239.
Luo Hui-lan, Chen Hong-kun. A review of object detection study based on deep learning[J]. Acta Electronica Sinica, 2020, 48(6): 1230-1239.
15 刘尚争, 刘斌, 高庆华.关于多维图像Criminisi自适应识别仿真研究[J].计算机仿真, 2020, 37(6): 379-382, 447.
Liu Shang-zheng, Liu Bin, Gao Qing-hua. Simulation study on criminisi adaptive recognition of multidimensional images[J]. Computer Simulation, 2020, 37(6): 379-382, 447.
[1] Xian-feng GUO,Hao-hua LI,Jin-yu WEI. Image encryption scheme based on Fibonacci transform and improved Logistic-Tent chaotic map [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 2115-2120.
[2] Pei-yong LIU,Jie DONG,Luo-feng XIE,Yang-yang ZHU,Guo-fu YIN. Surface defect detection algorithm of magnetic tiles based on multi⁃branch convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1449-1457.
[3] Zhen-hai ZHANG,Kun JI,Jian-wu DANG. Crack identification method for bridge based on BCEM model [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(5): 1418-1426.
[4] Yu JIANG,Jia-zheng PAN,He-huai CHEN,Ling-zhi FU,Hong QI. Segmentation-based detector for traditional Chinese newspaper [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1146-1154.
[5] Lin BAI,Lin-jun LIU,Xuan-ang LI,Sha WU,Ru-qing LIU. Depth estimation algorithm of monocular image based on self-supervised learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1139-1145.
[6] Jun-jie WANG,Yuan-jun NONG,Li-te ZHANG,Pei-chen ZHAI. Visual relationship detection method based on construction scene [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(1): 226-233.
[7] Hong-wei ZHAO,Jian-rong ZHANG,Jun-ping ZHU,Hai LI. Image classification framework based on contrastive self⁃supervised learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1850-1856.
[8] Xuan-jing SHEN,Xue-feng ZHANG,Yu WANG,Yu-bo JIN. Multi⁃focus image fusion algorithm based on pixel⁃level convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1857-1864.
[9] Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871.
[10] Na LI,Shao-sheng TAN. Image segmentation of fencing continuous action based on spatial neighborhood information [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1639-1644.
[11] Zhen WANG,Meng GAI,Heng-shuo XU. Surface reconstruction algorithm of 3D scene image based on virtual reality technology [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1620-1625.
[12] Sheng-sheng WANG,Lin-yan JIANG,Yong-bo YANG. Transfer learning of medical image segmentation based on optimal transport feature selection [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1626-1638.
[13] Huai-jiang YANG,Er-shuai WANG,Yong-xin SUI,Feng YAN,Yue ZHOU. Simplified residual structure and fast deep residual networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1413-1421.
[14] Ming LIU,Yu-hang YANG,Song-lin ZOU,Zhi-cheng XIAO,Yong-gang ZHANG. Application of enhanced edge detection image algorithm in multi-book recognition [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 891-896.
[15] Hai-yang JIA,Rui XIA,An-qi LYU,Ceng-xuan GUAN,Juan CHEN,Lei WANG. Panoramic mosaic approach of ultrasound medical images based on template fusion [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(4): 916-924.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!