Integrating Deep Learning with Correlation-based Multimedia Semantic Concept Detection

Ha, Hsin-Yu

doi:10.25148/etd.fidc000162

Cited by 2 publications

(1 citation statement)

References 131 publications

(169 reference statements)

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“…is study evaluates the approach both quantitatively and qualitatively. For evaluating our approach quantitatively, the metrics of precision, average precision (AP), mean average precision (mAP), recall, and the value of F1-score are used to estimate the accuracy of the models [51].…”

Section: Experimentation and Resultsmentioning

confidence: 99%

Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision

Saqlain

Rubab

Khan

et al. 2022

Mathematical Problems in Engineering

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In retail management, the continuous monitoring of shelves to keep track of the availability of the products and following proper layout are the two important factors that boost the sales and improve customer’s level of satisfaction. The studies conducted earlier were either performing shelf monitoring or verifying planogram compliance. As both the activities are important, to tackle this problem, we presented a deep learning and computer vision-based hybrid approach called Hyb-SMPC that deals with both activities. The Hyb-SMPC approach consists of two modules: The first module detects fine-grained retail products using one-stage deep learning detector. For the detection part, the comparison of three deep learning-based detectors, You Only Look Once (YOLO V4), YOLO V5, and You Only Learn One Representation (YOLOR), is provided and the one giving the best result will be selected. The selected detector will perform detection of different categories of SKUs and racks. The second module performs planogram compliance; for this purpose, the company-provided layout is first converted to JavaScript Object Notation (JSON) and then the matching is performed with the postprocessed retail images. The compliance reports will be generated at the end for indicating the level of compliance. The approach is tested in both quantitative and qualitative manners. The quantitative analysis demonstrates that the proposed approach achieved an accuracy up to 99% on the provided dataset of retail, whereas the qualitative evaluation indicates increase in sales and customers’ satisfaction level.

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Section: Experimentation and Resultsmentioning

confidence: 99%

Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision

Saqlain

Rubab

Khan

et al. 2022

Mathematical Problems in Engineering

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A Survey on Deep Learning

et al. 2018

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The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.

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Integrating Deep Learning with Correlation-based Multimedia Semantic Concept Detection

Cited by 2 publications

References 131 publications

Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision

Hybrid Approach for Shelf Monitoring and Planogram Compliance (Hyb-SMPC) in Retails Using Deep Learning and Computer Vision

A Survey on Deep Learning

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