Towards automatic smoke detector inspection: Recognition of the smoke detectors in industrial facilities and preparation for future drone integration

TL;DR

This paper develops and evaluates an automatic smoke detector recognition system suitable for drone inspection in industrial facilities, comparing various deep learning models and training strategies to ensure robustness in challenging real-world conditions.

Contribution

It introduces a smoke detector recognition method integrated with drone inspection, comparing multiple object detection models and training approaches for improved accuracy and robustness.

Findings

YOLOv11n achieved the highest mAP@0.5 of 0.884.

Model performance was tested on datasets with challenging conditions like motion blur and small objects.

The study provides publicly available code, models, and dataset for further research.

Abstract

Fire safety consists of a complex pipeline, and it is a very important topic of concern. One of its frontal parts are the smoke detectors, which are supposed to provide an alarm prior to a massive fire appears. As they are often difficult to reach due to high ceilings or problematic locations, an automatic inspection system would be very beneficial as it could allow faster revisions, prevent workers from dangerous work in heights, and make the whole process cheaper. In this study, we present the smoke detector recognition part of the automatic inspection system, which could easily be integrated to the drone system. As part of our research, we compare two popular convolutional-based object detectors YOLOv11 and SSD widely used on embedded devices together with the state-of-the-art transformer-based RT-DETRv2 with the backbones of different sizes. Due to a complicated way of collecting a sufficient amount of data for training in the real-world environment, we also compare several training strategies using the real and semi-synthetic data together with various augmentation methods. To achieve a robust testing, all models were evaluated on two test datasets with an expected and difficult appearance of the smoke detectors including motion blur, small resolution, or not complete objects. The best performing detector is the YOLOv11n, which reaches the average mAP@0.5 score of 0.884. Our code, pretrained models and dataset are publicly available.