学术动态：Real-Time Small Retail Product Detection in Low-Light Intelligent Cabinets Under Complex Backgrounds
MDPI Sensors 论文摘要：Intelligent retail cabinets require accurate and real-time detection of small retail products in complex environments, particularly under low-light conditions and large-scale variations. However, existi
论文标题：Real-Time Small Retail Product Detection in Low-Light Intelligent Cabinets Under Complex Backgrounds
发布日期：2026-05-21
作者：Moushiqi Yang, Junjie Cai, Yuanyuan Yang, Jian Chen, Kai Xie
DOI：10.3390/s26103264
论文摘要：Intelligent retail cabinets require accurate and real-time detection of small retail products in complex environments, particularly under low-light conditions and large-scale variations. However, existing object detection methods often suffer from insufficient feature representation and unstable performance in small-object retail commodity recognition tasks under low illumination and complex backgrounds. To address these challenges, this paper proposes a real-time small retail product detection framework based on YOLOv26 for low-light intelligent cabinet environments, aiming to improve detection accuracy, robustness, and deployment efficiency. A C3k2-enhanced multi-scale feature extraction module is introduced to strengthen feature representation for small retail products, while a novel detection head integrates minimum-resolution feature layers and an Efficient Multi-scale Attention (EMA) mechanism to enhance feature fitting ability under low-light conditions. In addition, convolution-based downsampling and Content-Aware ReAssembly of Features module (CARAFE) is adopted to improve feature fusion efficiency and reduce computational overhead. Experimental results on the RPC commodity dataset and the 6th Commodity Recognition Competition dataset demonstrate that the proposed method achieves improved detection performance compared with baseline models, with a 0.9% increase in Recall and a 0.2% improvement in mean Average Precision at IoU threshold 0.50 (mAP@50) while maintaining competitive mean Average Precision averaged over IoU thresholds from 0.50 to 0.95 (mAP@50-95). While the GFLOPS value rose from 5.8 to 6.3, deployment on the Jetson Nano platform achieves 25 FPS, demonstrating real-time detection capability in intelligent retail environments. The proposed framework provides a reliable and deployable solution for small retail product detection in low-light intelligent cabinet systems.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26103264. Vol. 26, Issue 10. Authors: Moushiqi Yang, Junjie Cai, Yuanyuan Yang, Jian Chen, Kai Xie.
开放许可： https://creativecommons.org/licenses/by/4.0/
原文链接： https://doi.org/10.3390/s26103264
PDF 链接： https://www.mdpi.com/1424-8220/26/10/3264/pdf
来源： MDPI Sensors via Crossref
