Early Stage Flame Segmentation with Deep Learning and Intel's OpenVINO Toolkit

Abstract

With the advancements of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), it is now possible to greatly speed up the processes of predicting certain anomalies and prevent unforeseen situations and disasters. One example of such an environmental disaster is the problem of early-stage flame segmentation. It is not only important to create a model capable of pattern recognition with high accuracy but also to optimize it for real-time execution. In this paper, we demonstrate the capabilities of Deeplabv3+ for early-stage flame segmentation on a custom-made dataset with challenging conditions, and near real-time execution with the adoption of the Open VINO toolkit. Acceleration of the inference process in the range of 70.46% to 93.46% is achieved, while the speed of the inference process when using the GPU with FP16 precision is increased by almost 2 times when compared to FP32 precision. The impact of our findings is significant, as early-stage flame segmentation is a critical component of disaster prevention in environmental settings. Our results demonstrate the potential of using the OpenVINO toolkit for the acceleration of the inference process.