2D keypoint detection plays an important role in the fields of group behavior analysis, motion capture, human-computer interaction, and security monitoring. However, in high-density crowd environments or edge devices with limited computational resources, it is still a major challenge to improve inference efficiency while ensuring detection accuracy. To this end, this paper proposes a keypoint detection framework called ‘DTMPose’, whose core innovation is to replace the computationally intensive attention module with a Mamba-based state-space model (SS2D mechanism) and to introduce a sense-field-enhanced convolution (e.g., ‘DPConv’) in the key parts, to improve the detection of local occlusion and edge details. Compared to models which only rely on the self-attention mechanism, DTMPose reduces the computational overheads, whilst still capturing global dependencies, and effectively mitigates local keypoint ambiguities through enhanced convolution. Experimental results on the COCO dataset show that DTMPose maintains a low parameter count with an accuracy of about 76% AP, demonstrating its deployment potential in high-density crowd scenarios and mobile edge devices, as well as providing a new feasible solution for applications such as people flow monitoring and group behavior analysis.