Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores
Providing high on-shelf availability (OSA) is a key factor to increase profits in grocery stores. Recently, there has been growing interest in computer vision approaches to monitor OSA. However, the largest and well-known computer vision datasets do not provide annotation for store products, and the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/2/327 |
| _version_ | 1797542238676320256 |
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| author | Ramiz Yilmazer Derya Birant |
| author_facet | Ramiz Yilmazer Derya Birant |
| author_sort | Ramiz Yilmazer |
| collection | DOAJ |
| description | Providing high on-shelf availability (OSA) is a key factor to increase profits in grocery stores. Recently, there has been growing interest in computer vision approaches to monitor OSA. However, the largest and well-known computer vision datasets do not provide annotation for store products, and therefore, a huge effort is needed to manually label products on images. To tackle the annotation problem, this paper proposes a new method that combines two concepts “semi-supervised learning” and “on-shelf availability” (SOSA) for the first time. Moreover, it is the first time that “You Only Look Once” (YOLOv4) deep learning architecture is used to monitor OSA. Furthermore, this paper provides the first demonstration of explainable artificial intelligence (XAI) on OSA. It presents a new software application, called SOSA XAI, with its capabilities and advantages. In the experimental studies, the effectiveness of the proposed SOSA method was verified on image datasets, with different ratios of labeled samples varying from 20% to 80%. The experimental results show that the proposed approach outperforms the existing approaches (RetinaNet and YOLOv3) in terms of accuracy. |
| first_indexed | 2024-03-10T13:26:53Z |
| format | Article |
| id | doaj.art-36876bfa69cc458c8aa2bc66d941bc8d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T13:26:53Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-36876bfa69cc458c8aa2bc66d941bc8d2023-11-21T08:46:30ZengMDPI AGSensors1424-82202021-01-0121232710.3390/s21020327Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery StoresRamiz Yilmazer0Derya Birant1Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, TurkeyDepartment of Computer Engineering, Dokuz Eylul University, Izmir 35390, TurkeyProviding high on-shelf availability (OSA) is a key factor to increase profits in grocery stores. Recently, there has been growing interest in computer vision approaches to monitor OSA. However, the largest and well-known computer vision datasets do not provide annotation for store products, and therefore, a huge effort is needed to manually label products on images. To tackle the annotation problem, this paper proposes a new method that combines two concepts “semi-supervised learning” and “on-shelf availability” (SOSA) for the first time. Moreover, it is the first time that “You Only Look Once” (YOLOv4) deep learning architecture is used to monitor OSA. Furthermore, this paper provides the first demonstration of explainable artificial intelligence (XAI) on OSA. It presents a new software application, called SOSA XAI, with its capabilities and advantages. In the experimental studies, the effectiveness of the proposed SOSA method was verified on image datasets, with different ratios of labeled samples varying from 20% to 80%. The experimental results show that the proposed approach outperforms the existing approaches (RetinaNet and YOLOv3) in terms of accuracy.https://www.mdpi.com/1424-8220/21/2/327on-shelf availabilitysemi-supervised learningdeep learningimage classificationmachine learningexplainable artificial intelligence |
| spellingShingle | Ramiz Yilmazer Derya Birant Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores Sensors on-shelf availability semi-supervised learning deep learning image classification machine learning explainable artificial intelligence |
| title | Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores |
| title_full | Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores |
| title_fullStr | Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores |
| title_full_unstemmed | Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores |
| title_short | Shelf Auditing Based on Image Classification Using Semi-Supervised Deep Learning to Increase On-Shelf Availability in Grocery Stores |
| title_sort | shelf auditing based on image classification using semi supervised deep learning to increase on shelf availability in grocery stores |
| topic | on-shelf availability semi-supervised learning deep learning image classification machine learning explainable artificial intelligence |
| url | https://www.mdpi.com/1424-8220/21/2/327 |
| work_keys_str_mv | AT ramizyilmazer shelfauditingbasedonimageclassificationusingsemisuperviseddeeplearningtoincreaseonshelfavailabilityingrocerystores AT deryabirant shelfauditingbasedonimageclassificationusingsemisuperviseddeeplearningtoincreaseonshelfavailabilityingrocerystores |