Design of a train-static balance positioning system based on multimodal large model

To address the requirement for raw material and finished product transport trains in the steel metallurgy industry to precisely align within the designated weighing zone during static weighbridge operations, this paper designs a visual positioning system based on the multimodal large model DeepSeek-VL2-tiny. The system captures images via high-definition industrial cameras deployed at the static weighbridge edges. It employs the DeepSeek-VL2-tiny model, fine-tuned using low-rank adaptive adjustment, to detect critical components in real time, including wheels, couplers, and weighbridge edge markers. By establishing a mapping relationship from pixel coordinates to physical space, it calculates the actual offset distance of the railcar relative to the standard weighing zone, thereby enabling intelligent guidance for parking positions. Experimental results in real industrial settings demonstrate the system's stable recognition and localization of critical components, validating the reliability of multimodal large models in practical industrial applications.