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Human part detection has significant research and application in computer vision fields such as human–robot interaction, motion capture, facial recognition, and human key point detection. However, the current human body part detection method encounters challenges when detecting multi-scale objects and capturing the correlation relationship between human instances and human parts. To address these problems, a new anchor-free instance-level human part detection network (AIP-Net) is proposed. AIP-Net is a "two-level" structure that consists of two lightweight anchor-free detectors: a body detector and a parts detector. AIP-Net gradually focuses the human body on the human part from top to down, effectively avoiding the interference of extraneous background and enhancing the correlation relationship between human instances and body parts. Additionally, we design a body-part multidimensional context (BPMC) model in the parts detector branch to enhance the capability of the network. We trained the AIP-Ne end-to-end and achieved a state-of-the-art (SOTA) performance of 36.2 mean average precision (mAP) on COCO Human Parts Dataset. Moreover, we successfully utilized the AIP-Net in the human–robot interaction(HRI) platform and validated its practicality. [Display omitted] • We propose a new anchor-free instance-level human part detection net- work (AIP-Net). • Both BodyDetector and PartDetector adopt an fully convolutional networks (FCN) anchor-free network structure. • We validated the performance of AIP-Net on the COCO Human Parts dataset, achieving state-of-the-art performance. [ABSTRACT FROM AUTHOR]