Yolov5-based Intelligent Detection Method for Retail Goods

Abstract

In the current context, intelligent unmanned retail checkout systems offer the prospect of efficient and innovative development. This study proposes an enhanced lightweight YOLOv5 merchandise detection and recognition method. The method introduces SELayer and a multi-headed self-attentive module of Transformer in YOLOv5 to enable the network to focus more on essential factors such as commodities when performing retail merchandise detection, and improve the recognition performance of the model. Also, the Ghost module is introduced to reduce network parameters and computation, increase computation speed and reduce latency. We validated the performance of the approach on a public dataset. Compared with the existing YOLOv5 model, the model achieves a 0.9% improvement in detection accuracy and a 27.7% reduction in GFLOPs. With this study, we optimise the problem of small batch identification of retail goods, providing a basis for automated processing of intelligent retail supply and marketing systems with practical implications.