Abstract
The current chopstick quality inspection machines on the market cannot effectively sort chopped chopsticks with burrs. Aiming at this problem, in this paper, a method for detecting burr defects of chopsticks based on improved YOLOv3 algorithm is proposed. By removing the 32x down-sampling detection layer in the YOLOv3 network multi-scale detection, adding a 4x down-sampling layer in the YOLOv3 network to further obtain deep features. Thereafter, it was fused with the shallow features in the second down-sampling, and let the network learn the deep and shallow features and re-cluster the anchor box size, with changing the hyper-parameters of the YOLOv3 network, including reducing jitter and the weight-decay regular term, and increasing the batch size. Finally, a suitable momentum value was selected to improve the original network. When IOU=50, the average detection accuracy of the improved network increased from 89% to 94%, and the accuracy rate increased by 4%, with the recall rate increasing by 9% and the average IOU increasing by 3.5%. The average detection speed increased from 16.8 to 21.0 frames per second. The experimental results showed that the method in this study had higher detection efficiency than the traditional chopstick quality inspection machine, which could meet the detection needs of chopstick burr defects.
Publication Date
3-28-2020
First Page
133
Last Page
138
DOI
10.13652/j.issn.1003-5788.2020.03.026
Recommended Citation
Jun-song, CHEN; Zi-fen, HE; and Yin-hui, ZHANG
(2020)
"Defect detection method of chopsticks based on improved YOLOv3 algorithm,"
Food and Machinery: Vol. 36:
Iss.
3, Article 26.
DOI: 10.13652/j.issn.1003-5788.2020.03.026
Available at:
https://www.ifoodmm.cn/journal/vol36/iss3/26
References
[1] 严晓明.有关一次性筷子的研究报[J].数学大世界,2013(34):32-33.
[2] 郭静,罗华,张涛.机器视觉与应用[J].电子科技,2014(7):185-188.
[3] 程鸿芳,张春友.自然场景下基于改进LeNet卷积神经网络的苹果图像识别技术[J].食品与机械,2019,35(3):155-158.
[4] 胡小慧,江虹,郭秋梅.基于图像分割的筷子瑕疵检测研究[J].微型机与应用,2016,35(2):39-42.
[5] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR),June 27-30,2016.Las Vegas,NV,USA.[S.l.]:IEEE,2016:779-788.
[6] REDMON J,FARHADIA.YOLO9000:better,faster,stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition.[S.l.]:IEEE,2017:7 263-7 271.
[7] 万磊,佟鑫,盛明伟,等.Softmax 分类器深度学习图像分类方法应用综述[J].导航与控制,2019,18(6):1-9.
[8] 林健巍.YOLO 图像检测技术综述[J].福建电脑,2019,35(9):80-83.
[9] LIU Wei,ANGUELOV D,ERHAN D,et al.SSD:Single shot multiBox detector[C]//European Conference on Computer Vision.Cham:Springer Press,2016:21-37.
[10] REN Shao-qing,HE Kai-ming,GIRSHICK R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems.Cambridge:MIT Press,2015:91-99.
[11] 丛思安,王星星.K-means 算法研究综述[J].计算机技术应用,2018(17):155-156.
[12] 王功鹏,段萌,牛常勇.基于卷积神经网络的随机梯度下降算法[J].计算机工程与设计,2018,39(2):441-445.