Evaluation of a Colorimetric Sensor System for Early Fire Detection

Pohle, Roland; Pohl, Teresa; Pannek, Carolin; Tarantik, Karina R.; Bauersfeld, Marie‐Luise; Wöllenstein, Jürgen; Raible, S.; Seiler, Frank

doi:10.3390/proceedings2130966

Eurosensors 2018 2018

DOI: 10.3390/proceedings2130966

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Evaluation of a Colorimetric Sensor System for Early Fire Detection

Roland Pohle

Teresa Pohl

Carolin Pannek

et al.

Abstract: The use of colorimetric reactions as a sensitive, selective and low-cost method for the detection of fire related gases is evaluated in this work. As the most relevant fire gas carbon monoxide CO is addressed in the first place. A rhodium-based metal-organic complex has been selected, which a colorimetric material sensitive for CO. For the optical readout a compact setup has been developed which provides the option for spectroscopy in transmission and attenuated total reflection in parallel. The detection of C… Show more

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2024

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FireYOLO-Lite: Lightweight Forest Fire Detection Network with Wide-Field Multi-Scale Attention Mechanism

Sheng,

Liang,

et al. 2024

Forests

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A lightweight forest fire detection model based on YOLOv8 is proposed in this paper in response to the problems existing in traditional sensors for forest fire detection. The performance of traditional sensors is easily constrained by hardware computing power, and their adaptability in different environments needs improvement. To balance the accuracy and speed of fire detection, the GhostNetV2 lightweight network is adopted to replace the backbone network for feature extraction of YOLOv8. The Ghost module is utilized to replace traditional convolution operations, conducting feature extraction independently in different dimensional channels, significantly reducing the complexity of the model while maintaining excellent performance. Additionally, an improved CPDCA channel priority attention mechanism is proposed, which extracts spatial features through dilated convolution, thereby reducing computational overhead and enabling the model to focus more on fire targets, achieving more accurate detection. In response to the problem of small targets in fire detection, the Inner IoU loss function is introduced. By adjusting the size of the auxiliary bounding boxes, this function effectively enhances the convergence effect of small target detection, further reducing missed detections, and improving overall detection accuracy. Experimental results indicate that, compared with traditional methods, the algorithm proposed in this paper significantly improves the average precision and FPS of fire detection while maintaining a smaller model size. Through experimental analysis, compared with YOLOv3-tiny, the average precision increased by 5.9% and the frame rate reached 285.3 FPS when the model size was only 4.9 M; compared with Shufflenet, the average precision increased by 2.9%, and the inference speed tripled. Additionally, the algorithm effectively addresses false positives, such as cloud and reflective light, further enhancing the detection of small targets and reducing missed detections.

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FireYOLO-Lite: Lightweight Forest Fire Detection Network with Wide-Field Multi-Scale Attention Mechanism

Sheng,

Liang,

et al. 2024

Forests

View full text Add to dashboard Cite

show abstract

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Evaluation of a Colorimetric Sensor System for Early Fire Detection

Cited by 1 publication

References 5 publications

FireYOLO-Lite: Lightweight Forest Fire Detection Network with Wide-Field Multi-Scale Attention Mechanism

FireYOLO-Lite: Lightweight Forest Fire Detection Network with Wide-Field Multi-Scale Attention Mechanism

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