LSR-YOLO: A lightweight and fast model for retail products detection.

ABSTRACT: Advanced computer vision techniques, particularly deep learning-based object detection, are enhancing the accuracy and efficiency of product identification in retail settings, driving the integration of intelligent systems within urban environments and smart cities. To address the high computational cost and slow detection speed of existing methods, this study proposes LSR-YOLO, a lightweight object detection framework based on YOLOv8n, designed for deployment in robots and intelligent devices. The model introduces architectural optimizations, including the CSPHet-CBAM attention module, to strengthen feature representation, followed by a channel pruning algorithm tailored to the new architecture to reduce redundancy while maintaining accuracy. Experiments on the Locount dataset demonstrate

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12543153 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature