Design Features of Grocery Product Recognition Using Deep Learning

被引:7
作者
Gothai, E. [1 ]
Bhatia, Surbhi [2 ]
Alabdali, Aliaa M. [3 ]
Sharma, Dilip Kumar [4 ]
Kondamudi, Bhavana Raj [5 ]
Dadheech, Pankaj [6 ]
机构
[1] Kongu Engn Coll, Dept Comp Sci & Engn, Erode 638060, Tamil Nadu, India
[2] King Faisal Univ, Dept Informat Syst, Coll Comp Sci & Informat Technol, Riyadh 11533, Saudi Arabia
[3] King Abdulaziz Univ, Fac Comp Informat Technol, Riyadh, Saudi Arabia
[4] Jaypee Univ Engn & Technol, Dept Math, Guna 473226, Madhya Pradesh, India
[5] Inst Publ Enterprise, Dept Management Studies, Hyderabad 500101, Telangana, India
[6] Swami Keshvanand Inst Technol, Dept Comp Sci & Engn, Management & Gramothan, Jaipur 302017, Rajasthan, India
关键词
Deep learning; product recognition; YOLOV5; accuracy; grocery store; precision; recall;
D O I
10.32604/iasc.2022.026264
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
At a grocery store, product supply management is critical to its employee's ability to operate productively. To find the right time for updating the item in terms of design/replenishment, real-time data on item availability are required. As a result, the item is consistently accessible on the rack when the client requires it. This study focuses on product display management at a grocery store to determine a particular product and its quantity on the shelves. Deep Learning (DL) is used to determine and identify every item and the store's supervisor compares all identified items with a preconfigured item planning that was done by him earlier. The approach is made in II-phases. Product detection, followed by product recognition. For product detection, we have used You Only Look Once Version 5 (YOLOV5), and for product recognition, we have used both the shape and size features along with the color feature to reduce the false product detection. Experimental results were carried out using the SKU-110 K data set. The analyses show that the proposed approach has improved accuracy, precision, and recall. For product recognition, the inclusion of color feature enables the reduction of error date. It is helpful to distinguish between identical logo which has different colors. We can achieve the accuracy percentage for feature level as 75 and score level as 81.
引用
收藏
页码:1231 / 1246
页数:16
相关论文
共 29 条
[1]  
Ajoodha R, 2017, 2017 PATTERN RECOGNITION ASSOCIATION OF SOUTH AFRICA AND ROBOTICS AND MECHATRONICS (PRASA-ROBMECH), P122, DOI 10.1109/RoboMech.2017.8261134
[2]  
Chaudhary S., 2017, 4 IEEE INT C IMAGE I, P1
[3]   Research On An Efficient Single-Stage Multi-Object Detection Algorithm [J].
Chen, Xin ;
Li, Jing .
2019 INTERNATIONAL CONFERENCE ON SMART GRID AND ELECTRICAL AUTOMATION (ICSGEA), 2019, :461-464
[4]   Object Detection with Discriminatively Trained Part-Based Models [J].
Felzenszwalb, Pedro F. ;
Girshick, Ross B. ;
McAllester, David ;
Ramanan, Deva .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2010, 32 (09) :1627-1645
[5]   Information Management for Intelligent Retail Environment: The Shelf Detector System [J].
Frontoni, Emanuele ;
Mancini, Adriano ;
Zingaretti, Primo ;
Placidi, Valerio .
INFORMATION, 2014, 5 (02) :255-271
[6]   Fine-Grained Product Class Recognition for Assisted Shopping [J].
George, Marian ;
Mircic, Dejan ;
Soros, Gabor ;
Floerkemeier, Christian ;
Mattern, Friedemann .
2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOP (ICCVW), 2015, :546-554
[7]   Precise Detection in Densely Packed Scenes [J].
Goldman, Eran ;
Herzig, Roei ;
Eisenschtat, Aviv ;
Goldberger, Jacob ;
Hassner, Tal .
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019), 2019, :5222-5231
[8]   What Makes for Effective Detection Proposals? [J].
Hosang, Jan ;
Benenson, Rodrigo ;
Dollar, Piotr ;
Schiele, Bernt .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (04) :814-830
[9]   Object detection method based on dense connection and feature fusion [J].
Jiang LiJia ;
Jiang JiaFu .
2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020), 2020, :1736-1741
[10]   Multiclass Object Detection in UAV Images Based on Rotation Region Network [J].
Xiao J. ;
Zhang S. ;
Dai Y. ;
Jiang Z. ;
Yi B. ;
Xu C. .
IEEE Journal on Miniaturization for Air and Space Systems, 2020, 1 (03) :188-196