To address the issue of accurate detection and identification of faint targets in geosynchronous orbit space under the background of starry sky, a multiple inspection object detection algorithm based on Hough transform is proposed. This study analyzes the characteristics of space targets in a geosynchronous orbit and the difficulties in detection and identification, as well as the shortcomings of traditional target detection algorithms. By using the continuous multi-frame images through denoising, threshold segmentation, centroid extraction, and star map matching, the influence of most of the stars is filtered out. The multi-frame images are then superimposed using Hough transform, and multiple tests are conducted to achieve accurate target extraction, which significantly improves the applicability of Hough transform in the detection of weak targets in space. The effectiveness of proposed algorithm is verified through field experiments and simulation data analysis. Compared with the traditional Hough algorithm, the detection accuracy is increased by 62.5%, the false alarm rate is reduced by 74.9%, and the time consumption of the algorithm is reduced by 7.2%; moreover, the detection accuracy is greater than 98% and the false alarm rate is less than 2% when the signal-to-noise ratio is greater than or equal to 3.