# Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis

arXiv: https://arxiv.org/abs/2609.02219 · HTML: https://mlsanity.com/paper/2609.02219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 2 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Data-Free Quantization Through Weight Equalization and Bias Correction](https://mlsanity.com/paper/1906.04721/) (2019)
- [GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training](https://mlsanity.com/paper/2009.03294/) (2020)
- [Adaptive Quantization for Deep Neural Network](https://mlsanity.com/paper/1712.01048/) (2017)
- [On the Impact of Calibration Data in Post-training Quantization and Pruning](https://mlsanity.com/paper/2311.09755/) (2023)
- [Towards Robust Detection of Adversarial Examples](https://mlsanity.com/paper/1706.00633/) (2017)
- [Automatic characterization of boulders on planetary surfaces from high-resolution satellite images](https://mlsanity.com/paper/2401.07528/) (2024)
- [Safe-NEureka: a Hybrid Modular Redundant DNN Accelerator for On-board Satellite AI Processing](https://mlsanity.com/paper/2602.04803/) (2026)
- [Visual Servoing for Robotic On-Orbit Servicing: A Survey](https://mlsanity.com/paper/2409.02324/) (2024)
- [Analysis of Single Event Induced Bit Faults in a Deep Neural Network Accelerator Pipeline](https://mlsanity.com/paper/2512.00028/) (2025)

## Abstract (arXiv)

Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light conditions, limited onboard compute, and radiation-induced hardware faults that can silently corrupt inference. We present a deployment-oriented instance segmentation framework for resource-constrained lunar robotics that jointly addresses quan- tization calibration and system-level fault exposure under strict compute constraints. First, we introduce Activation Variance Informative Sampling (AVIS), a label-free calibration strategy that deterministically selects calibration samples based on activation variance statistics. Second, we deploy a YOLO-based segmentation model on a Deep Learning Processor Unit (DPU) with architectural modifications that reduce CPU fallback paths and enable statically compiled execution with bounded latency in low-lighting conditions. We further introduce a software-level criticality analysis to estimate fault exposure and guide mitigation under radiation-constrained operation. On a lunar micro-rover platform, AVIS with bias correction recovers 69.8% of quantization-induced accuracy loss while achieving 309 ms inference latency and 5.7 W power consumption. Targeted mitigation reduces global criticality by 31.7%. The results demonstrate an integrated approach and a blueprint for a reliable and safe AI perception framework under space deployment constraints.