To address the issues of low detection accuracy, high missed detection rate, and poor realtime performance in complex indoor and outdoor scenarios, where the instrument area occupies a small pixel ratio due to the long shooting distance, this paper proposes an improved pointer instrument detection algorithm based on YOLOv8, named GRCP-YOLOv8. First, a C2f_CGA module, integrated with the CGA attention mechanism, is designed to enhance the model′s ability to express features at different scales and replace all C2f modules in the backbone network. Secondly, RFAConv is introduced to replace the conventional convolution layers, addressing the insufficient feature representation caused by parameter sharing in standard convolution modules. Subsequently, a new neck network structure, CCFPN is designed. By incorporating high-resolution feature maps extracted from the backbone network, it improves the model′s capability to detect small targets, while reducing the number of channels in convolution layers via 1×1 convolutions, thus reducing the model′s parameter count and computational complexity. Finally, a new detection head, RepHead, based on reparameterized convolution (RepConv), is introduced to reduce computational load and memory consumption during inference. Experimental results show that the proposed algorithm achieves accuracy, recall rate, and mAP@50 of 94.3%, 91.6%, and 92.5%, respectively, with recall and mAP@50 improving by 1.3% and 1.2% compared to the YOLOv8n model. The algorithm also reduces computational complexity and parameter count by 39% and 27%, respectively, while the model size is only 4.22 MB. These results demonstrate that the proposed algorithm not only improves detection accuracy but is also more suitable for deployment on edge devices.