CAE-YOLOv11n: Research on Commodity Classification for Retail Automated Checkout Systems

Traditional retail checkout processes often face inefficiencies, including long wait times and human errors, which negatively impact customer satisfaction and operational efficiency. This paper proposes CAE-YOLOv11n, an advanced version of the YOLOv11n object detection model, tailored for automated product classification in retail checkout systems. By incorporating three innovative modules-Convolutional Block Attention Module (CBAM), Adaptive Spatial Feature Fusion (ASFF), and Efficient IoU (EIoU)-CAE-YOLOv11n enhances feature extraction, multi-scale feature fusion, and localization accuracy. These improvements enable the model to effectively address the challenges of recognizing diverse retail products with high precision. Evaluation results demonstrate exceptional recognition accuracy and performance, highlighting the model’s potential to streamline checkout operations, reduce errors, and improve the shopping experience. This work advances retail automation and offers a practical solution for deployment in realworld retail environments.