IMPLEMENTATION OF A ONE STAGE OBJECT DETECTION SOLUTION TO DETECT COUNTERFEIT PRODUCTS MARKED WITH A QUALITY MARK

Abstract

Counterfeit products are a major problem that the market has faced for a long time. According to the Global Brand Counterfeiting Report 2018, "Amount of Total Counterfeiting, globally has reached to 1.2 Trillion USD in 2017 and is Bound to Reach 1.82 Trillion USD by the Year 2020" a solution to this concern has already been researched and published by the authors in previous research papers published in e-society 2020 and IADIS journal. However, the issue with the previously mentioned solution was that the object detection performance and accuracy in detecting small objects need to be improved. In this paper, a comparison between the current state of the art algorithm YOLO (You Only Look Once) used in the new implementation and the SSD (Single Shot Detector) algorithm, the faster R-CNN (Region-Based Convolutional Neural Networks) used in the old implementation is made under the same condition and using the same training, testing, and validation sets. The comparison is made in the context of the present task to discuss and prove why YOLO is a more suitable option for the counterfeit product detection task.