MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning

Baoxi Yuan, Yang Li, Fan Jiang, Xiaojie Xu, Yingxia Guo, Jianhua Zhao, Deyue Zhang, Jianxin Guo and Xiaoli Shen
Additional contact information
Baoxi Yuan: School of Information Engineering, Xijing University, Xi’an 710123, China
Yang Li: Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi’an 710068, China
Fan Jiang: Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi’an 710068, China
Xiaojie Xu: Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi’an 710068, China
Yingxia Guo: Dongfanghong Middle School, Anding District, Dingxi City 743000, China
Jianhua Zhao: School of Information Engineering, Xijing University, Xi’an 710123, China
Deyue Zhang: Unit 95949 of CPLA, HeBei 061736, China
Jianxin Guo: School of Information Engineering, Xijing University, Xi’an 710123, China
Xiaoli Shen: Xi’an Haitang Vocational College, Xi’an 710038, China

Future Internet, 2019, vol. 11, issue 9, 1-25

Abstract: In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation (IS) can detect the position of tags while also segmenting each instance. Currently, the mask region-based convolutional neural network (Mask R-CNN) method is used to realize IS, but the completeness of the instance mask cannot be guaranteed. Furthermore, due to the rich texture of QR tags, low-quality images can lower intersection-over-union (IoU) significantly, disabling it from accurately measuring the completeness of the instance mask. In order to optimize the IoU of the instance mask, a QR tag IS method named the mask UNet region-based convolutional neural network (MU R-CNN) is proposed. We utilize the UNet branch to reduce the impact of low image quality on IoU through texture segmentation. The UNet branch does not depend on the features of the Mask R-CNN branch so its training process can be carried out independently. The pre-trained optimal UNet model can ensure that the loss of MU R-CNN is accurate from the beginning of the end-to-end training. Experimental results show that the proposed MU R-CNN is applicable to both high- and low-quality images, and thus more suitable for Industry 4.0.

Keywords: quick response (QR); instance segmentation; dice loss; Mask R-CNN; Mask scoring R-CNN; UNet; product traceability system (PTS); visual navigation; automated guided vehicle (AGV); unmanned aerial vehicle (UAV) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
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