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According to our greatest understanding, various applications can be applied to the smoke‐fire detection and alarm system on cargo ships. Such as hardware‐based like ANSUL or software‐based like FireNet proposed in this paper. In this paper, we are going to propose a fire‐smoke detection and alarm system, called YlFireman. This system is multi‐purpose, which can detect fire and smoke in a millisecond, tell the bridge room which holds to have fire event, upload the fire or smoke event onto the cloud, and mobile users (those operator/managers) can get the message at once and plan for the firefighting strategies, trigger the carbon dioxide into the holds that have a fire or smoke events. YlFireman might operate faster and more humanized than the traditional approaches, such as ANSUL. Moreover, YlFireman is also highly maintainable and updatable since the core functionality is realized by YOLOV5 real‐time object detection. Thanks to this approach, we can always keep the technologies up‐to‐date because we might be able to update this system with the state‐of‐art model proposed from those top-tier conferences yearly, which is almost impossible for hardly feasible for traditional approaches.


Statista https://www.statista.com/statistics/419055/causes-of-losses-of- ships-worldwide/
AutoMotive News https://www.autonews.com/manufacturing/ estimated-loss-cargo-ship-fire-rises-335-million-report-says#:∼: text=The%20cargo%20ship%20that%20caught,from%20%24282% 20million%20on%20Friday
ANSUL https://www.ansul.com/en/us/pages/default.aspx
Fire and Smoke (FM) Dataset https://public.roboflow.com/ object-detection/wildfire-smoke

Cited by

Wang, Y. (2022). Development Status and Trend of Flame Detection based on Deep Learning. International Core Journal of Engineering, 8(8), 61-64. https://doi.org/10.6919/ICJE.202208_8(8).0008