Smoke detectors are a critical part of any fire safety system today. With new standards being rolled out for both residential and commercial smoke detectors, the requirements that fire safety products must comply with have become even stricter. Smoke detector manufacturers must now face challenges like detecting fires earlier, reducing false alarms for nuisance smoke such as that found in cooking, and more. Machine learning, but specifically at the edge, may prove useful to addressing these challenges as it allows inference to be performed with minimal latency and low power consumption, two key aspects of any robust smoke detector design.
This is a companion discussion topic for the original entry at https://www.edgeimpulse.com/blog/using-embedded-machine-learning-to-perform-smoke-detection