Quantized deep learning for low-cost IR sensors with layer-wise relevance propagation

Abstract

In recent years, the market for infrared (IR) sensors has expanded from traditional defense and security applications to include consumer products. As a result, there is increasing demand for embedded IR systems that integrate low-cost sensors with embedded processors. Meanwhile, deep learning has made significant advances and achieved superhuman performance in some domains. To support deep learning in embedded systems, new processors have emerged that are specifically designed for running deep neural networks. In this paper, we propose a performance evaluation method for applying quantized deep learning to low-cost IR sensors using layer-wise relevance propagation. Our method provides visualized analysis of what the neural networks learn. We demonstrate the effectiveness of our approach through experiments on a low-cost IR sensor dataset, showing that our method achieves an explainable performance evaluation method for degraded cases arising from the tradeoff between speed and accuracy of quantized detectors which is a typical problem in embedded systems with limited computational resources.