南京理工大学学报（自然科学版）2024，Vol.48Issue(1)：74-81,8.DOI:10.14177/j.cnki.32-1397n.2024.48.01.007
一种用于智能零售视觉结算的增量学习方法
Incremental learning method for intelligent retail automatic check-out
摘要
Abstract
To deal with the incremental learning issue of intelligent retail automatic check-out,a novelty data-argument-based triplet model is proposed,which consists of the synthesizer network,the renderer network and the detector network.Specifically,the synthesizer network and the renderer network learn collaboratively to generate rendered check-out images with distribution close to the real data by synthesizing and rendering the single-product example images with data augmentation.In the incremental learning phase,the original and new product example images are collaborative learned to generate rendered check-out images containing new products.These rendered check-out images are utilized to train the product detector network.The model obtained by training in this way has the ability to recognize both of original products and new products.The experimental results show that the model has more excellent ability to overcome catastrophic forgetting compared to the existing incremental learning methods.The incremented check-out accuracy is 64.90% with a forgetting rate of 3.63% ,which is better than the state-of-the-art method of 4.38% .关键词
视觉结算/增量学习/合成/渲染/目标检测Key words
automatic check-out/incremental learning/synthesizer/renderer/object detection分类
信息技术与安全科学引用本文复制引用
陈昊,魏秀参,肖亮..一种用于智能零售视觉结算的增量学习方法[J].南京理工大学学报（自然科学版）,2024,48(1):74-81,8.基金项目
国家重点研发计划青年科学家项目(2021YFA1001100) （2021YFA1001100）
国家自然科学基金(62272231 （）
61871226) （）
江苏省自然科学基金青年基金项目(BK20210340) （BK20210340）
中国人工智能学会-华为MindSpore学术奖励基金(CAAIXSJLJJ-2022-001B) （CAAIXSJLJJ-2022-001B）
江苏省地质局科研项目(2023KY11) （2023KY11）
