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A Mask RCNN-based Bottle Recognition and Counting System for Retail Shelf Intelligence
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 13 - Number 2 |
| Year of Publication: 2026 |
| Authors: Cesar Robles, Temitope Oladokun, Chukwuemeka C. Ugwu, Gbolahan Kolawole, Taiwo Adelakin, Saleem Adebayo |
10.5120/ijais2026452046
|
Cesar Robles, Temitope Oladokun, Chukwuemeka C. Ugwu, Gbolahan Kolawole, Taiwo Adelakin, Saleem Adebayo . A Mask RCNN-based Bottle Recognition and Counting System for Retail Shelf Intelligence. International Journal of Applied Information Systems. 13, 2 ( Mar 2026), 34-43. DOI=10.5120/ijais2026452046
Abstract
Accurate identification and counting of bottles are important for effective inventory management in bottling operations. However, many bottling operations still rely on manual audits and conventional object detection techniques that struggle in visually complex environments. In this study, deep learning-based computer vision approach is proposed to enable precise and automated real-time bottle identification and counting. An automated system was developed using Mask R-CNN-based instance segmentation approach. The methodology includes the collection of chiller bottle images, image pre-processing, mask RCNN model training, evaluations, and deployment. The performance of the proposed model was compared with faster RCNN using two backbone networks, ResNet-50 and ResNet-101. The result obtained shows that mask RCNN outperformed faster RCNN in extracting region of interest (ROI) extraction, with significant difference in average precision of 5.07% and 2.08% for the ResNet50 and ResNet101 respectively. These findings show the effectiveness of Mask R-CNN for accurate bottle detection and counting in automated bottling inventory systems.
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