Algorithmic and Machine Learning Approaches for Space Debris Tracking, Image Enhancement, and Mitigation

The exponential proliferation of space debris and satellite mega-constellations strictly necessitates the modernization of Space Traffic Management away from human-in-the-loop manual assessments towards fully autonomous, AI-driven architectures. This paper has comprehensively analyzed the algorithmic frontier of space sustainability. By deploying advanced Convolutional Neural Networks (UNet-ResNet34) alongside deformable convolution modules (YOLO-DBS) and tracking-by-detection frameworks (SDT-Net), optical observation systems can now isolate and track hypervelocity, low-SNR micro-debris that previously evaded traditional radar and photometric models. Furthermore, modeling Active Debris Removal as a Markov Decision Process solved via Masked Proximal Policy Optimization transforms static, heuristic-based mission planning into a dynamic, adaptive framework capable of optimizing fuel expenditure while autonomously dodging dynamically manifesting collision zones. Moving forward, the fusion of these deep learning models with Quantum Annealing for route optimization and NanoTug swarms for physical capture will ultimately dictate the long-term viability and safety of the Low Earth Orbit environment.

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