Aiming at the recognition failure of traditional 2D code detection methods for assisted navigation in complex industrial, logistics and transport scenarios, this study proposes an improved and lightweight YOLOv8-AR model to further enhance the recognition efficiency by using AR codes that are mature and can provide relative positional information. In terms of the network model, the backbone introduces an ultra-strong lightweight StarNet network to reduce the algorithmic computation of target detection; the C2f-EMSC module is optimised and constructed in the neck network to enhance the extraction of AR code features in complex environments and reduce the computational load at the same time; moreover, a lightweight detail-enhanced shared convolutional detection head LSDECD-H is proposed to improve the detail feature expression capability, so as to improve the detection accuracy of small targets and multi-targets. The experimental results show that the parametric and computational quantities of the model are 1.46M and 4.7GFLOPs, which are only 51% and 42% of the baseline, and the mAP is as high as 0.962 with high robustness in the case that the frame rate meets the real-time detection. It can quickly determine the position before decoding, and improve the recognition effect to achieve precise positioning, making it suitable for application scenarios like 2D code road sign navigation.