Deep Feature-Based Three-Stage Detection of Banknotes and Coins for Assisting Visually Impaired People

Abstract

Owing to the rapid advancements in smartphone technology, there is an emerging need for a technology that can detect banknotes and coins to assist visually impaired people using the cameras embedded in smartphones. Previous studies have mostly used handcrafted feature-based methods, such as scale-invariant feature transform or speeded-up robust features, which cannot produce robust detection results for banknotes or coins captured in various backgrounds and environments. With the recent advancement in deep learning technology, some studies have been conducted on banknote and coin detection using a deep convolutional neural network (CNN). However, these studies also showed degraded performance depending on the changes in background and environment. To overcome these drawbacks, this paper proposes a three-stage detection technology for new banknotes and coins by applying faster region-based CNN, geometric constraints, and the residual network (ResNet). In the experiment performed using the open database of Jordanian dinar (JOD) and 6,400 images of eight types of Korean won banknotes and coins obtained using our smartphones, the proposed method exhibited a better detection performance than the state-of-the-art methods based on handcrafted features and deep features.