Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet
Rongjun Chen, Hongxing Huang, Yongxing Yu, Jinchang Ren, Wang Peixian, Huimin Zhao, Xu Lu
Abstract
Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding-based Internet of Things (IoT) systems. To tackle this issue, we propose in this article a rapid detection approach, which consists of multistage stepwise discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The average detection precision (ADP), multiple box rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications.