Mask R-CNN with New Data Augmentation Features for Smart Detection of Retail Products

Abstract

Human–computer interactions (HCIs) use computer technology to manage the interfaces between users and computers. Object detection systems that use convolutional neural networks (CNNs) have been repeatedly improved. Computer vision is also widely applied to multiple specialties. However, self-checkouts operating with a faster region-based convolutional neural network (faster R-CNN) image detection system still feature overlapping and cannot distinguish between the color of objects, so detection is inhibited. This study uses a mask R-CNN with data augmentation (DA) and a discrete wavelet transform (DWT) in lieu of a faster R-CNN to prevent trivial details in images from hindering feature extraction and detection for deep learning (DL). The experiment results show that the proposed algorithm allows more accurate and efficient detection of overlapping and similarly colored objects than a faster R-CNN with ResNet 101, but allows excellent resolution and real-time processing for smart retail stores.