物联网学报2023，Vol.7Issue(4)：142-152,11.DOI:10.11959/j.issn.2096-3750.2023.00366
基于位置可学习视觉中心机制的零售商品检测方法
Retail commodity detection method based on location learnable visual center mechanism
摘要
Abstract
To address the problem of low detection accuracy caused by the difficulty in effectively capturing significant and diversified feature information for packaging deformation and overlap products,a location learnable visual center(LLVC)mechanism was designed to improve YOLOX-s,achieving higher detection accuracy.To effectively deal with product packaging deformation and overlap phenomena,firstly,global context information was captured through a light-weight multi-layer perceptron to help the model better understand spatial information in product features.Secondly,the local feature representation ability was enhanced by the designed LLVC and the spatial information was used to allocate learnable weights for local features to increase the attention of discriminative local features.Finally,the intersection over union(IoU)loss function was replaced with centered intersection over union(CIoU)and power parameters were intro-duced on this basis to effectively reduce the missed detection rate.Experimental results show that the proposed method achieves an accuracy of 91.3%on the retail product checkout(RPC)dataset,which is 2.2%higher than YOLOX-s and better than current mainstream lightweight object detection algorithms.At the same time,frame per second(FPS)is 97 frame/s,and the model size is 9.48 MB.It can accurately and in real-time detect retail products in scenarios where computing resources are limited.关键词
零售商品检测/YOLOX-s/中心学习机制/损失函数/轻量级Key words
retail commodity detection/YOLOX-s/central learning mechanism/loss function/lightweight分类
信息技术与安全科学引用本文复制引用
吕晓华,魏铭辰,刘立波..基于位置可学习视觉中心机制的零售商品检测方法[J].物联网学报,2023,7(4):142-152,11.基金项目
国家自然科学基金资助项目(No.62262053) （No.62262053）
宁夏科技创新领军人才计划项目(No.2022GKLRLX03)The National Natural Science Foundation of China(No.62262053),The Ningxia Science and Technology Inno-vation Leading Talent Plan(No.2022GKLRLX03) （No.2022GKLRLX03）
