福建电脑2024，Vol.40Issue(4)：30-34,5.DOI:10.16707/j.cnki.fjpc.2024.04.007
引入CoordConv卷积的管制物品检测网络设计
Design of Controlled Item Detection Network by Introducing CoordConv Convolution
摘要
Abstract
The detection algorithm for controlled items with small targets has the problem of poor performance.To solve this problem,this paper proposes an improved YOLOv5 controlled item detection algorithm.Firstly,the CoordConv module is introduced into the network to represent the coordinates of feature map pixels.Then,the C2f module is introduced into the network to simultaneously utilize both detail and semantic information at different scales,improving the network's feature extraction ability and receptive field.The performance verification results of the algorithm show that compared to the YOLOv5 algorithm,our algorithm performs better on the Easy,Hard,and Hidden test sets mAP@.5.95 increased by 2.5,1.8,and 4.4 percentage points respectively,indicating that the detection accuracy of the algorithm in this paper is relatively high.关键词
管制物品检测/小目标/检测算法Key words
Controlled Substance Detection/Small Target/Detection Algorithm分类
信息技术与安全科学引用本文复制引用
何松,刘文鑫,陈鑫..引入CoordConv卷积的管制物品检测网络设计[J].福建电脑,2024,40(4):30-34,5.基金项目
本文得到江西省研究生创新专项(No.YC2023-S662)资助. （No.YC2023-S662）
