［Objective］In rail transit inspection and monitoring, accurate mileage information of the track plays a critical role in the efficient utilization of inspection data. Therefore, it is necessary to study and design a precise sleeper localization technology.［Method］Due to certain errors in mileage positioning from images captured by track video inspection systems, the YOLOv8n object detection model is adopted to locate sleepers and ground electronic tags in inspection images and perform mileage correction. Based on the YOLOv8n model, a new EIoU (enhanced intersection over union) method is used. Through optimization of loss function and structural constraints on sleeper counting, a sleeper object detection model YOLOv8n_SC is proposed based on the improved YOLOv8n model. A structural optimization algorithm is innovatively introduced, and the sleeper counting issue when sleepers are split across two adjacent images is solved, and structural constraints are provided to mitigate missed and duplicate detections. Taking a city metro line in Guangzhou as an example, the improved YOLOv8n_SC model is applied to detect sleepers and electronic tags along the line and perform mileage correction. Thus, the scenarios of sleeper positioning missed and false detections are greatly improved.［Result & Conclusion］The improved and constrained sleeper positioning model YOLOv8n_SC significantly enhances sleeper positioning accuracy, achieving a sleeper-level precise mileage positioning method with 100% accuracy. Without requiring additional system equipment, the YOLOv8n_SC model improves the mileage accuracy of the inspection image acquisition system, providing strong practical feasibility.