Object Recognition with SSD MobileNet Pre-Trained Model in the Cashier Application

Nazil Ilham Burhanudin; Arif Dwi Laksito; Acihmah Sidauruk; Muhammad Resa Arif Yudianto; Alfie Nur Rahmi

doi:10.32736/sisfokom.v12i2.1659

Authors

Nazil Ilham Burhanudin Universitas Amikom Yogyakarta
Arif Dwi Laksito Universitas Amikom Yogyakarta
Acihmah Sidauruk Universitas Amikom Yogyakarta
Muhammad Resa Arif Yudianto Universitas Muhammadiyah Magelang
Alfie Nur Rahmi Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.32736/sisfokom.v12i2.1659

Keywords:

Object detection, MobileNet, Tensorflow

Abstract

Object recognition is a type of image processing technique that is frequently employed in current applications such as facial identification, vehicle detection, and automated cashiers. One issue with barcode and RFID cashier apps is that they cannot scan several products at the same time. The cashier application employing object identification using picture images is believed to be able to distinguish more than one object in order to speed up the transaction process. The usage of SSD pre-trained models with MobileNet architecture to detect items in automatic cashier applications is discussed in this paper. This study put the model to the test on three types of soft drink objects: coca-cola, floridina, and good day. A smartphone camera was used to collect the data, which totaled 203 images. The findings indicated that the product object identification method was 82.9% accurate, 97.5% precise, and 84.7% recall. The object recognition process takes between 365 and 827 milliseconds, with an average time of 695 milliseconds (0.69 seconds).

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