基于深度学习的图像检测算法在快消品零售行业的应用

Application of Image Detection Algorithm Based on Deep Learning in the Retail Industry of Fast Moving Consumer Goods

随着快消行业线下快速发展,各大厂家纷纷抢占线下市场,文章针对快消零售行业的主要业务场景,即业务员对货架商品的陈列识别和商品清点统计,提出基于深度学习神经网络的图像识别和目标检测的货架商品识别的方案。该方案针对快消行业场景特色选择模型,进行预处理图像和提取特征等操作,从而提高模型识别准确度和速度。设计方案通过模型给出包括商品的单品品类数、排面数等关键数据信息,对提高员工效率,降低企业成本,推动零售行业的线下扩张与发展,具有重要的现实意义。

With the rapid development of the fast-moving consumer goods industry offline,major manufacturers are vying to occupy the offline market.This article focuses on the main business scenarios of the fast-moving consumer retail industry:salespersons'display recognition of shelf goods,product inventory statistics,and proposes a scheme for shelf product recognition based on deep learning neural networks for image recognition and object detection.Based on the characteristics of the fast-moving consumer goods industry scene,models are selected,images are preprocessed,and features are extracted,Improved model accuracy and speed.Providing key data information such as the number of individual product categories and layouts through models is of great practical significance in improving employee efficiency,reducing human and material costs,and promoting offline expansion and development of the retail industry.