软件导刊2025，Vol.24Issue(2)：19-25,7.DOI:10.11907/rjdk.241104
CountNet:一种用于堆叠胶合板计数的自监督学习框架
CountNet:A Self-Supervised Learning Framework for Automated Stacked Plywood Counting
摘要
Abstract
Automated counting of stacked plywood materials is a major challenge in industrial production.Traditional methods based on manu-al counting and physical counting are time-consuming and inefficient.However,stacked plywood images are often affected by factors such as uneven edges and irregular thickness,leading to inaccurate counting results with existing deep learning algorithms due to the lack of strong representational features extracted.To address these issues,we propose a self-supervised learning framework,CountNet,for counting stacked plywood materials.CountNet introduces a novel loss function that leverages the advantages of contrastive learning to further amplify the differ-ences between positive and negative samples,enabling the network to extract more representative visual features.These features are then uti-lized in downstream tasks to achieve accurate counting.Experimental results demonstrate that the proposed method outperforms other common counting models in terms of accuracy,loss reduction,and various other metrics,showcasing its superiority in counting capability.关键词
自监督对比学习/计算机视觉/堆叠胶合板计数/数据增强技术/损失函数优化Key words
self supervised contrastive learning/computer vision/stacked plywood counting/data augmentation/optimization of loss func-tion分类
信息技术与安全科学引用本文复制引用
苏凡,王若琪,王海涛..CountNet:一种用于堆叠胶合板计数的自监督学习框架[J].软件导刊,2025,24(2):19-25,7.基金项目
广东省自然科学基金项目(2021A1515011319) （2021A1515011319）
