计算机应用与软件2024，Vol.41Issue(4)：129-134,158,7.DOI:10.3969/j.issn.1000-386x.2024.04.020
基于自适应注意力机制的YOLOv4无人超市商品检测
YOLOV4 FOR UNMANNDE SUPERMARKET COMMODITY DETECTION BASED ON ADAPTIVE ATTENTION MECHANISM
摘要
Abstract
Aimed at the problems of low real-time commodity detection,poor detection of stacked commodities,and difficulties in classifying similar commodities in the intelligent settlement task of unmanned supermarkets,a commodity recognition algorithm based on improved YOLOv4 is proposed.The algorithm used the lightweight network MobileNetv2 for feature extraction to speed up the detection speed.The channel attention and spatial attention were introduced into the inverted residual structure of MobileNetv2 to amplify the local feature weight,and thus enhancing the detection ability of stacked commodities.Focal loss was used in the loss function to solve the difficult classification problem with small inter-class difference.The experimental results show that this method achieves 80.3％accuracy and a detection speed of 73 FPS on self-built product data sets,which is better than the YOLOv4 algorithm.关键词
无人超市/商品检测/注意力机制/深度可分离卷积Key words
Unmanned supermarket/Commodity detection/Attention mechanism/Depthwise separable convolution分类
信息技术与安全科学引用本文复制引用
章超华,丁胜,苏浩..基于自适应注意力机制的YOLOv4无人超市商品检测[J].计算机应用与软件,2024,41(4):129-134,158,7.基金项目
国家自然科学基金项目(61806150) （61806150）
福建省大数据管理新技术与知识工程重点实验室开放课题(BD201805). （BD201805）
