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Spatial-Aware Product Detection
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AI
DOI：10.1109/tcsvt.2026.3730788.png)
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Abstract
En 中文
Due to factors such as dense placement, small size, and similar appearance, accurately detecting products on retail shelves remains a significant challenge. To address these challenges, most of the existing methods use advanced detectors directly or focus only on improving feature representation capability during feature extraction. However, these methods ignore the spatial characteristics of products on shelves, i.e., items in the same category or with similar attributes are often clustered and exhibit similar scales, resulting in sub-optimal performance. In this paper, we propose a novel product detector, called SAPDet, to better learn the spatial distribution properties of shelf items. In particular, a Hybrid Information Capture (HIC) module is used to integrate 1) the product distribution information obtained by clustering and 2) the product structure information obtained by high-frequency enhancement after FPN. We formulate them as visual tokens in the proposed two-stream attention mechanism, boosting the spatial representation of features. Following the HIC module, the Attention-based Iterative Regression (AIR) leverages a cross-attention mechanism to further integrate the aforementioned information into the feature map, refining boundary box predictions through precise offset corrections. These designs effectively facilitate the spatial characteristics learning of products and improve the performance of the detector in shelf scenarios. Experimental results demonstrate the superiority of SAPDet over state-of-the-art models on publicly available datasets. On the most challenging Unitail dataset, SAPDet gains 4.1% AP improvements. Our code will be released soon.
Keywords:
Object detection
Spatial information
Product detection
Journal
IF:
11.1
Papers:
865
Citations:
3.1W
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