[2508.18025] Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration

Computer Science > Machine Learning arXiv:2508.18025 (cs) [Submitted on 25 Aug 2025 (v1), last revised 4 Mar 2026 (this version, v3)] Title:Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration Authors:Aditri Paul, Archan Paul View a PDF of the paper titled Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration, by Aditri Paul and 1 other authors View PDF HTML (experimental) Abstract:Autonomous planetary exploration demands real-time, high-fidelity environmental perception. Standard deep learning models, however, require far more memory and compute than space-qualified, radiation-hardened, power-optimized hardware can provide. This limitation creates a severe design bottleneck. Engineers struggle to deploy sophisticated detection architectures without overloading the strict power and memory limits of onboard computers of outer space planetary exploration platforms. In this foundational concept paper, we propose the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys) to resolve this bottleneck. We present an architectural blueprint integrating a Quantized Neural Network (QNN), refined through Quantization Aware Training (QAT), with an Adaptive Multi-Sensor Fusion (AMF) module and Multi-Scale Detection Heads. By forcing weights into low-precision integer arithmetic during the training and optimization phase, our framework strips away the floating-point overhead that typically overwhelms onboard compu...