Upload
cristiano-rafael-steffens
View
32
Download
0
Embed Size (px)
Citation preview
A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile RobotsCristiano Steffens, Ricardo Nagel Rodrigues, and Silvia Silva da Costa Botelho
Universidade Federal do Rio Grande – FURG
Centro de Ciências Computacionais
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 2
About
Challenges: Clutter and scene background, Uncontrolled fire flames can assume a variety of
characteristics Can hardly be described using any of the feature
descriptorsthat are widely used for object recognition.
Approach: Color spectroscopy Texture Spatial information.
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 3
A brief overview on the state-of-the-art
Phillips (2002) Chen (2004) Toreyin (2005) Çelik (2007, 2008, 2010) Li (2011, 2012) Kolesov (2010) Mueller (2013)
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 4
A brief overview on the state-of-the-art
Borges (2010) Chenebert (2011)
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 5
DatasetVideos
24 videos 28k frames (51.37% contain fire) 17k annotated regions Creative Commons 3.0 license Variety of fire sources
Uneven illumination Camera movement Different color accuracy settings Clutter Partial Occlusion Motion blur Scale and projection Reflection
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 6
DatasetAnnotations
Rectangle that embraces the whole fire region Very small fire sparkles left out One frame may present zero or many annotations
XML files Each video file has its corresponding annotation file Average flame area is 61512px (~250×250px square) Fire region size/frame size = 8,92%
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 7
Our Proposal
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 8
Random Forests
RFs are o combination of tree classifiers Proposed by Breiman et al. (2001) Attributes are randomly chosen Each tree classifies the sample independently The final class is given by pooling Each tree is built using 2/3 random samples of the
training set
Can deal with many attributes Are easy to understand Have a linear complexity (after training) Each tree can be executed in parallel
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 12
Results
Gren – Only colorBlue – Color + Texture and Temporal
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 13
Results
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 14
Results
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 15
Results
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 16
Results
Cross Validation: Train/ Validation/ Test
MetricsÇelik (2010)
Zhou (2010)
Chenebert (2011) Ours
Recall (TPR) 0.739 0.987 0.990 0.831 Specificity (SPC) 0.317 0.022 0.724 0.988Precision (PPV) 0.654 0.638 0.857 0.982NPV 0.410 0.501 0.979 0.884 FPR 0.682 0.977 0.275 0.012 FDR 0.345 0.361 0.142 0.018 FNR 0.260 0.012 0.009 0.168 Accuracy (ACC) 0.585 0.635 0.890 0.920F1 Score 0.694 0.775 0.919 0.900MCC 0.060 0.036 0.773 0.843
05/01/2023 A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots 17
Thank you!
A Texture Driven Approach for Visible Spectrum Fire Detection on Mobile Robots
Thanks to: