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 Detection for Control Valve） 2023. The image of this dataset came from the control valve production workshop， and after the image collection was completed， it underwent steps such as dataset preprocessing， dataset annotation， and dataset partitioning. The images of this dataset were all from the control valve production 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， including 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 conditions 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 effectiveness 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 oriented object detection algorithms.