A parameter identification method based on a lightweight RT-DETR model and a spline interpolation method is proposed for the instrument intelligent inspection system developed for the offshore platform wellhead control panel. To achieve lightweight model and high-precision targets extraction, the backbone and neck networks of RT-DETR are reconstructed and optimized. The cross-stage local illumination enhancement module (CSP-IEM) and the fast enhanced hybrid aggregation module (FEMAM) are introduced to improve illumination robustness and neck network detection efficiency. A matching-aware loss function (MAL) is designed to preserve matching quality information. The ablation experimental results show that the model achieves a mean average precision (mAP) of 76.1% at 0.5, reduces the number of parameters and computation by 30.73% and 25.79%, respectively, and achieves a frame rate of 216 fps. Based on the key targets recognition and image processing methods for instruments, a method using spline interpolation to calculate instrument readings is proposed. The experiment of reading instrument parameters from on-site collected images shows that the spline interpolation method reduces the mean relative error and mean global error by 45.5% and 48.3%, respectively compared to the local angle method. Comprehensive experiments have demonstrated that the proposed instrument parameter identification method meets the requirements for deployment under limited on-site computing resources, as well as the needs for detection accuracy and real-time performance.