Accurate and efficient spacecraft detection is critical for mitigating collision risks in Earth's increasingly crowded orbital environment, where the proliferation of space debris threatens to escalate the Kessler Syndrome a cascading cycle of collisions that generate catastrophic debris fields. This study presents a YOLOv8n-based preliminary framework optimized for real-time spacecraft detection in synthetic images derived from NASA's Pose Bowl Challenge dataset. By performing rigorous preprocessing including bounding box area filtering (75th percentile, ≤ 2500 px2) and resolution reduction from HR to 256 × 256 pixels, we mitigate noise from structural outliers and maintain computational efficiency. Transfer learning on YOLOv8n achieved a validation mAP50–95 of 0.73 at 125 FPS, demonstrating near real-time capability critical for timely collision avoidance, even with limited hyperparameter tuning during 5 epochs and 10 iterations. Training stopped at epoch 356 for a patience value of 25, with peak performance at epoch 212 achieving 0.85 and 1.11 for mAP50 and box loss respectively. While domain gaps from synthetic data and suboptimal convergence highlight the need for deeper architectural refinement, this work underscores the potential of lightweight detection systems to enhance space situational awareness. By enabling rapid identification of spacecraft and debris, the proposed preliminary approach could reduce collision risks, mitigate debris generation, and contribute to long-term orbital sustainability.