In the intelligentization process of open-pit mines, small target detection for unmanned mining trucks is confronted with technical challenges caused by complex surface conditions and extreme multi-scale target distributions. An enhanced lightweight small target detection algorithm, PCSF-RTDETR, is proposed in this study based on the RT-DETR frame work, with specific optimization for open-pit mining environments. Firstly, the lightweight PConv-Block module is integrated into the backbone feature extraction network, which enables effective capture of small target characteristics while significantly reducing computational complexity. Secondly, a cascaded group attention mechanism is introduced into the intra-scale feature interaction module, through which the processes of feature focusing and noise suppression are collaboratively optimized. Furthermore, through the integration of a dynamic upsampler, multi-scale feature fusion, and the Slimneck architecture, the Slimneck-DSF cross-scale feature fusion framework is proposed; this framework enhances the adaptability to multi-scale targets while reducing the computational load of the network. Finally, the Focaler-GIoU loss function is adopted, which incorporates a dynamic focusing mechanism. This mechanism differentially adjusts the weight allocation for samples with varying difficulty levels, thereby significantly improving the accuracy of small target detection. Experimental results demonstrate that, compared with the baseline RT-DETR model, PCSF-RTDETR achieves a 31.5 % reduction in parameter count and a 23.4 % reduction in FLOPs, along with a 4.7 % improvement in detection accuracy and an 8.2 % improvement in detection speed. Notably, the mAP50 metric reaches 92.5 %, which conclusively validates the effectiveness of the proposed PCSF-RTDETR-based lightweight small target detection method for open-pit mining scenarios.