AI-Based Lunar Hazard Detection for Autonomous Landing

Autonomous lunar landing is a foundational prerequisite for next-generation planetary exploration, particularly across lunar polar regions characterized by severe communication latencies and complex topographic features that preclude real-time human intervention. While accurate hazard assessment encompassing craters, boulders, extreme slopes, and permanently shadowed regions is imperative for mission survival, ambient low-light conditions yield surface imagery dominated by sub-visual contrast, low visibility, and sensor noise. To circumvent these imaging constraints, this paper introduces an end-to-end computer vision framework that couples low-light image restoration with semantic hazard classification for optimal landing zone selection. The core architecture relies on a multi-stage deep learning pipeline: LLFormer first amplifies micro-illumination features to expose hidden topography, followed by a Restormer network configured to systematically attenuate noise and reconstruct fine structural details. Downstream, a specialized object detection model maps localized surface hazards, feeding these spatial constraints into a terrain suitability algorithm that computes localized safety profiles. Evaluated against standard image quality metrics (PSNR, SSIM) and object detection benchmarks (Precision, Recall, F1-Score, mAP), experimental validation demonstrates that integrating low-light enhancement prior to object detection significantly boosts downstream classification accuracy and feature clarity. Ultimately, this framework provides a robust framework for intelligent, vision-guided navigation and real-time decision-support in autonomous planetary landings.