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).