“Significant progress has been made in a study targeting practical industrial scenarios. The research team has released a dense control valve parts dataset called PD4CV (Part Detection for Control Valve) 2023, providing new resources for automatic object detection in industrial production. This dataset originates from the control valve production workshop and contains 9 types of parts, 510 images of workbenches, and 15015 part samples. It has characteristics such as dense placement, occlusion, large size differences, and similar appearances, which pose many challenges for automatic object detection. Through comparative experiments, the research team found that general scenario datasets and specific industrial scenario datasets are difficult to cope with the specificity of the PD4CV2023 dataset. However, a comprehensive comparison of a series of object detection algorithms on this dataset has validated its effectiveness, demonstrating the superiority of the PD4CV2023 dataset in general object detection, multi-scale object detection, small-scale, and imbalanced data object detection. This research achievement provides a new direction for industrial object detection research and is expected to promote the automation and intelligence process in industrial production. At the same time, this dataset also provides valuable experimental resources for researchers in related fields, laying a solid foundation for solving object detection problems in industrial automation.”