Research Article
Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision
Table 1
Comparison of proposed approach with the previous approaches.
| Reference no. | Year | Object detection | Planogram compliance | Methods | Traditional method | Deep learning | Two-stage | One-stage |
| [4] | 2015 | | | | ✓ | SURF | [16] | 2015 | ✓ | | | | SURF | [18] | 2015 | | ✓ | | | SVM | [19] | 2015 | | | | ✓ | SURF + color histogram | [22] | 2016 | | | | ✓ | Recurring pattern detection | [38] | 2016 | | | | ✓ | CNN | [36] | 2017 | | ✓ | | | VGG-F | [17] | 2018 | ✓ | | | | SIFT | [21] | 2018 | | ✓ | | | VGG-16 with recurring features and attention maps | [37] | 2018 | | ✓ | | | GoogLeNet | [35] | 2019 | | ✓ | | | CIFAR-10, CaffeNet | [43] | 2020 | | | ✓ | | RetinaNet, YOLO V3, YOLO V4 | [44] | 2020 | | | | ✓ | VGG-16, Tiny YOLO V2 | Proposed method | | | ✓ | ✓ | YOLO V4, YOLO V5, YOLOR |
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