Automated Model Hardening with Reinforcement Learning for On-Orbit Object Detectors with Convolutional Neural Networks

Author:

Shi Qi,Li Lu,Feng Jiaqi,Chen Wen,Yu Jinpei

Abstract

On-orbit object detection has received extensive attention in the field of artificial intelligence (AI) in space research. Deep-learning-based object-detection algorithms are often computationally intensive and rely on high-performance devices to run. However, those devices usually lack space-qualified versions, and they can hardly meet the reliability requirement if directly deployed on a satellite platform, due to software errors induced by the space environment. In this paper, we evaluated the impact of space-environment-induced software errors on object-detection algorithms through large-scale fault injection tests. Aside from silent data corruption (SDC), we propose an extended criterial SDC-0.1 to better quantify the effect of the transient faults on the object-detection algorithms. Considering that a bit-flip error could cause severe detection result corruption in many cases, we propose a novel automated model hardening with reinforcement learning (AMHR) framework to solve this problem. AMHR searches for error-sensitive kernels in a convolutional neural network (CNN) through trial and error with a deep deterministic policy gradient (DDPG) agent and has fine-grained modular-level redundancy to increase the fault tolerance of the CNN-based object detectors. Compared to other selective hardening methods, AMHR achieved the lowest SDC-0.1 rates for various detectors and could tremendously improve the mean average precision (mAP) of the SSD detector by 28.8 in the presence of multiple errors.

Publisher

MDPI AG

Subject

Aerospace Engineering

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