Successful autonomous planetary exploration hinges on real-time, high-fidelity environmental perception. However, standard deep learning models usually demand far more memory and computation power than space-qualified, radiation-hardened onboard hardware can provide. This creates a fundamental design challenge of deploying sophisticated detection architectures without saturating the rigid power and memory envelopes of the computation hardware of planetary exploration platforms. We propose the Adaptive Quantized Planetary Crater Detection System to resolve this bottleneck. Our framework integrates a Quantized Neural Network, refined through Quantization Aware Training, with an Adaptive Multi-Sensor Fusion module. By forcing weights into low-precision integer arithmetic, we effectively strip away the floating-point overhead that typically bottlenecks onboard processors and system memory. This yields a leaner model footprint and significantly faster processing while the detection fidelity remains high. Such efficiency enables AMF module to merge high-bandwidth Optical Imagery streams with Digital Elevation Models using an Adaptive Weighting Mechanism to re-balance sensor priority under variable conditions like deep shadows or high albedo. Integrated Multi-Scale Detection Heads then resolve craters across a wide range of diameters, providing a computationally efficient and precise solution for real-time detection, localization of craters and hazard avoidance. This paper establishes the architectural design and theoretical justification of the system. While our methodology is grounded in principles of hybrid computer vision and planetary science, we present this as a blueprint for future empirical validation and hardware benchmarking on integer-arithmetic units. This system provides a capability vital for the next generation of autonomous landing, navigation, and deep space explorations.

翻译：成功的自主行星探索依赖于实时、高保真的环境感知。然而，标准深度学习模型通常需要比经过太空认证、抗辐射的星载硬件所能提供的多得多的内存和计算能力。这造成了一个根本性的设计挑战：如何在部署复杂检测架构的同时，不超出行星探索平台计算硬件严格的功耗和内存预算。我们提出了自适应量化行星陨石坑检测系统来解决这一瓶颈。我们的框架将经过量化感知训练优化的量化神经网络与自适应多传感器融合模块相结合。通过将权重强制转换为低精度整数运算，我们有效消除了通常制约星载处理器和系统内存的浮点运算开销。这产生了更精简的模型占用空间和显著更快的处理速度，同时保持了较高的检测保真度。这种效率使得自适应多传感器融合模块能够利用自适应加权机制，将高带宽光学图像流与数字高程模型融合，在深阴影或高反照率等变化条件下重新平衡传感器优先级。集成的多尺度检测头随后可解析各种直径的陨石坑，为陨石坑的实时检测、定位和危险规避提供了一个计算高效且精确的解决方案。本文阐述了该系统的架构设计和理论依据。虽然我们的方法基于混合计算机视觉和行星科学原理，但我们将其作为未来在整数运算单元上进行实证验证和硬件基准测试的蓝图。该系统为下一代自主着陆、导航和深空探索提供了至关重要的能力。