Improved Space Object Detection Based on YOLO11
Yi Zhou, Tianhao Zhang, Zijing Li, Jianbin Qiu · Aerospace · 2025
Space object detection, as the foundation for ensuring the long-term safe and stable operation of spacecraft, is widely applied in a variety of close-proximity tasks such as non-cooperative target monitoring, space debris avoidance, and spacecraft mission planning. To strengthen the detection capabilities for non-cooperative spacecraft and space debris, a method based on You Only Look Once Version 11 (YOLO11) is proposed in this paper. On the one hand, to tackle the issues of noise and low contrast in images captured by spacecraft, bilateral filtering is applied to remove noise while preserving edge and texture details effectively, and image contrast is enhanced using the contrast-limited adaptive histogram equalization (CLAHE) technique. On the other hand, to address the challenge of small object detection in spacecraft, loss-guided online data augmentation is proposed, along with improvements to the YOLO11 network architecture, to boost detection capabilities for small objects. The experimental results show that the proposed method achieved 99.0% mAP50 (mean Average Precision with an Intersection over Union threshold of 0.50) and 92.6% mAP50-95 on the SPARK-2022 dataset, significantly outperforming the YOLO11 baseline, thereby validating the effectiveness of the proposed method.