In order to fulfill the requirements for various operations in space, such as rendezvous, docking, and capturing, there is a pressing need to achieve ultraclose-range spacecraft pose measurement. This paper addresses the challenges of pose measurement under low-light conditions at ultraclose range by introducing a stereovision solution based on target detection and adaptive circle extraction. Initially, an improved target detection algorithm is employed to expedite feature object detection. Subsequently, an adaptive circle extraction algorithm is developed through analysis of camera imaging to surmount challenges related to feature extraction and potential feature loss in the space environment. This approach facilitates swift and accurate measurement of spacecraft at ultraclose range. The results showcase a 66.36% reduction in parameter count for the enhanced target detection algorithm compared with the prevalent YOLOv7_tiny algorithm. Additionally, the adaptive circle extraction algorithm demonstrates an 11.4% increase in cooperative target feature extraction precision compared with existing methods while maintaining requisite detection speed. Simulation experiments indicate that the real-time position measurement error for spacecraft at ultraclose range is less than 0.18 mm, and angle measurement error is less than 0.05°. This presents a viable visual solution for spacecraft pose measurement at ultraclose range in low-light environments.