Earticle

초록

영어

Out-of-Stock (OOS) detection is a critical task in retail management, yet traditional automated methods suffer from significant limitations. Existing approaches, whether based on classical image processing or closed-set deep learning models, lack scalability and robustness, often requiring extensive retraining for every new shelf layout or product type. This paper proposes a novel and flexible OOS detection pipeline that leverages the power of Open- Vocabulary Object Detection (OVD). Instead of attempting to directly detect "empty space," our method first identifies all present products using an OVD model guided by flexible text prompts. An inverse occupancy mask is then generated to identify potential OOS regions, which are subsequently refined through a robust multi-stage post-processing filter. Experiments on our custom dataset of 149 diverse retail shelf images demonstrate the superiority of this approach. Our method achieves an OOS detection Accuracy of 87.9%, vastly outperforming the baseline approach (70.1% Accuracy). Furthermore, by applying per-shelf optimized prompts and parameters, our model's Accuracy increases to 96.84%, highlighting its high adaptability and effectiveness for realworld retail environments.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Geometric Preprocessing
B. Open-Vocabulary Product Detection
C. Inverse Occupancy Mask Generation
D. Mask Postprocessing
III. EXPERIMENTS AND RESULTS
A. Experiment Settings
B. Experimental Results
IV. CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES

저자

• Rakwon Choi [ Safe AI Seoul, South Korea ]
• Junwon Son [ Safe AI Seoul, South Korea ]
• Wonsuk Kim [ Safe AI Seoul, South Korea ]
• Wonsuk Kim [ Safe AI Seoul, South Korea ]

참고문헌