AQ-PCDSys: An Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration

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.