基于深度学习的商品识别方法与检测算法研究

Research on Goods Recognition Methods and Detection Algorithms Based on Deep Learning

由于人们对美好生活的向往愈发强烈,消费已经成为拉动我国经济发展的重要引擎,而在消费过程中强化消费体验也是提升消费者服务效益的关键所在。为了能够在提升消费体验的同时降低人力的投入,引入智能化商品识别工具,研究一种利用注意力机理进行特征抽取与学习的方法。文章简要介绍了深度学习方法和基于深度学习的商品识别方法,探讨了深度学习多目标商品检测算法,对比分析了改进后的MaskR-CNN,可有效防止因网络复杂性的提高而造成的性能下降,从而提高了检测效率和检测精度。

Due to people's growing desire for a better life,consumption has become an important engine driving China's economic development,and strengthening consumer experience in the consumption process is also the key to improving consumer service efficiency.In order to improve the consumer experience while reducing human investment,an intelligent goods recognition tool is introduced to study a method of feature extraction and learning using attention mechanism.This paper briefly introduces deep learning methods and deep learning-based goods recognition methods,explores deep learning multi-objective goods detection algorithms,and compares and analyzes the improved MaskR-CNN,which can effectively prevent performance degradation caused by the increase in network complexity,thereby improving detection efficiency and accuracy.