GondoCheck: A Vision-Based Backend for Automated Shelf Auditing

Leonardo Brito, Jonas Silva, Andrea Maria N. C. Ribeiro, João Teixeira

DOI: 10.14209/sbrt.2025.1571157280

Evento: XLIII Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2025)

Keywords: Retail computer vision out-of-stock detection metric learning ResNet-152

Abstract

The persistent out-of-stock problem in Brazilian retail erodes revenue and customer loyalty. We present GondoCheck, a cloud backend that receives a single photo of a gondola, detects the stocked items, matches them to a target stock-keeping-unit (SKU) list through deep metric learning and returns real-time shelf-share analytics. Using 96 real supermarket images and six dishwashing-liquid SKUs, the system achieves 85.8% global accuracy and 0.77 macro F1, with 100% precision on two shelves after fine-tuning a ResNet-152 embedding. The approach shows that combining off-the-shelf object detectors with metric-learning embeddings can deliver practical, scalable shelf auditing without planograms or store calibration.

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