为太空探测设计轻量级陨石坑检测系统，兼顾精度与资源限制。
Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration
- 采用INT8量化神经网络与感知训练，降低计算开销。
- 融合光学影像与高程图，支持极端光照下稳定检测。
- 无锚框设计配合局部浮点转换，精准识别不规则陨石坑。
自主行星探测需要实时、高保真的环境感知能力。标准深度学习模型需大量计算资源，而航天器上搭载的计算机在功耗、散热和内存方面有严格限制。这种矛盾造成严重工程瓶颈，阻碍了高性能感知架构在地外探测平台上的部署。本文提出自适应量化陨石坑检测系统（AQ-PCDSys）的理论架构，构建针对量化感知训练（QAT）优化的INT8量化神经网络（QNN）。为应对传感器脆弱性，数学化定义自适应多传感器融合（AMF）模块，通过推导空间注意力门控所需的精确整数重量化系数，在特征层面动态选择并融合光学影像（OI）与数字高程模型（DEM），确保在极端光照变化和光学硬件故障时仍能可靠感知。此外，系统引入无锚框、中心到边缘回归头，并以局部FP16坐标转换保护，避免整数截断导致的灾难性误差，准确框定非对称月球陨石坑。本文未展示物理硬件数据，而是建立系统的理论边界、结构逻辑与数学依据，并提出严格的硬件在环（HITL）评估协议，明确未来实证验证所需测试标准，为下一代航天任务软件设计铺平道路。
原文摘要 · Abstract (English)
Autonomous planetary exploration demands real-time, high-fidelity environmental perception. Standard deep learning models require massive computational resources. Conversely, space-qualified onboard computers operate under strict power, thermal, and memory limits. This disparity creates a severe engineering bottleneck, preventing the deployment of highly capable perception architectures on extraterrestrial exploration platforms. In this foundational concept paper, we propose the theoretical architecture for the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys) to resolve this bottleneck. We present a mathematical blueprint integrating an INT8 Quantized Neural Network (QNN) designed specifically for Quantization Aware Training (QAT). To address sensor fragility, we mathematically formalize an Adaptive Multi-Sensor Fusion (AMF) module. By deriving the exact integer requantization multiplier required for spatial attention gating, this module actively selects and fuses Optical Imagery (OI) and Digital Elevation Models (DEMs) at the feature level, ensuring reliable perception during extreme cross-illuminations and optical hardware dropouts. Furthermore, the architecture introduces anchor-free, center-to-edge regression heads, protected by a localized FP16 coordinate conversion, to accurately frame asymmetrical lunar craters without catastrophic integer truncation. Rather than presenting physical hardware telemetry, this manuscript establishes the theoretical bounds, structural logic, and mathematical justifications for the architecture. We outline a rigorous Hardware-in-the-Loop (HITL) evaluation protocol to define the exact testing criteria required for future empirical validation, paving the way for next-generation space-mission software design.
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