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2019 | OriginalPaper | Buchkapitel

Naïve Approach for Bounding Box Annotation and Object Detection Towards Smart Retail Systems

verfasst von : Pubudu Ekanayake, Zhaoli Deng, Chenhui Yang, Xin Hong, Jang Yang

Erschienen in: Security, Privacy, and Anonymity in Computation, Communication, and Storage

Verlag: Springer International Publishing

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Abstract

It is becoming a trend that companies use smart retail stores to reduce the selling cost, by using the sensor technologies. Deep convolutional neural network models which are pre-rained for the Object detection task achieve state-of-the-art result in many benchmark. However, when applying these algorithms to the intelligent retail system to help automated checkout, we need to reduce the manual labelling cost of making retail data sets, and to achieve real-time demand while ensuring accuracy. In our paper, we propose a naive approach to get first portion of the bounding box annotations for a given custom image dataset in order to reduce manual cost. Experimental results show that our approach helps to label the first set of images in short time of period. Further, the custom module we designed helped to reduce the number of parameters by 41.77% for the YOLO model maintaining the original model’s accuracy (85.8 mAP).

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Vorheriges Kapitel CP-MCNN: Multi-label Chest X-ray Diagnostic Based on Confidence Predictor and CNN

Nächstes Kapitel Evaluation of Face Recognition Techniques Based on Symlet 2 Wavelet and Support Vector Machine

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Titel: Naïve Approach for Bounding Box Annotation and Object Detection Towards Smart Retail Systems
verfasst von: Pubudu Ekanayake
Zhaoli Deng
Chenhui Yang
Xin Hong
Jang Yang
Verlag: Springer International Publishing
Buch: Security, Privacy, and Anonymity in Computation, Communication, and Storage
Print ISBN: 978-3-030-24899-4

Electronic ISBN: 978-3-030-24900-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-24900-7_18

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