光学精密工程2024，Vol.32Issue(8)：1241-1251,11.DOI:10.37188/OPE.20243208.1241
面向工业目标检测的密集控制阀零件数据集
Dense control valve parts dataset for industrial object detection
摘要
Abstract
Automated intelligence in industrial production is inseparable from automatic object detection,and high-accuracy automatic object detection relies on datasets adapted to the actual scene.This article published a dense control valve parts dataset for industrial practical scenarios,named PD4CV(Part Detec-tion for Control Valve)2023.The image of this dataset came from the control valve production work-shop,and after the image collection was completed,it underwent steps such as dataset preprocessing,da-taset annotation,and dataset partitioning.The images of this dataset were all from the control valve pro-duction workshop.After the image collection was completed,the dataset images were first preprocessed,followed by labeling the part targets in the dataset images.Then,the dataset images were divided into training,validation,and testing sets.The PD4CV2023 dataset covered a total of 9 types of parts,includ-ing 510 workstation images and 15 015 part samples,with an average of approximately 29 part samples per image.Compared with the existing object detection datasets,this dataset had the characteristics of dense placement and occlusion of parts,large size differences of parts,similar shapes of some parts,and unbalanced number of parts samples.Finally,pre training comparative experiments on different types of datasets show that general scenario datasets and specific industrial scenario datasets are only suitable for general and specific tasks,while the PD4CV2023 dataset,which represents the actual production condi-tions of control valves,can be used for target detection of control valve parts,and has its particularity and irreplaceability;a comprehensive comparison of a series of algorithms on this dataset verifies the effective-ness of PD4CV2023 dataset in general object detection,multi-scale object detection,and object detection under small-scale and imbalanced data.The PD4CV2023 dataset can be used for research on industrial ori-ented object detection algorithms.关键词
深度学习/工业目标检测/数据集/控制阀零件Key words
deep learning/industrial object detection/dataset/control valve parts分类
信息技术与安全科学引用本文复制引用
王琳毅,白静,李彦梅,李文静..面向工业目标检测的密集控制阀零件数据集[J].光学精密工程,2024,32(8):1241-1251,11.基金项目
国家自然科学基金项目(No.62162001) （No.62162001）
宁夏自然科学基金项目(No.2022AAC02041) （No.2022AAC02041）
宁夏优秀人才支持计划项目 （）
北方民族大学研究生创新项目(No.YCX23150) （No.YCX23150）
