REAL TIME FIRE DETECTION USING FASTER R-CNN MODEL

Year 2019, Volume: 3 Issue: 3, 220 - 226, 31.12.2019

Hilmi Saygın Sucuoğlu , İsmail Böğrekci , Pınar Demircioğlu

Abstract

The
aim of this study is to develop a real time fire detector using Faster R-CNN (Faster
region-based convolutional neural network). For machine learning process of the
system; 1,000 images (including fire and non-fire scenes) 80 and 20% for
training and validation, respectively were used. The machine learning process
was conducted using a system with the specifications of NVidia GeForce GTX 1070
Ti with 17 GB onboard memory. The required environments and tools (Python 3.5,
Tensorflow 1.13.1, OpenCV, CUDA-cuDNN toolkits) were installed on the Anaconda
virtual environment. The fire scenes on the images were labeled as fire and
non-fire using LabelImg software. The metrics of the training process were
obtained from the Tensorboard. The total loss value decreased from 2 to 0.02 with
the steps of 40,000 at training. As the loss function was lower than the level
of 0.05, inference graph was frozen and exported to detect the fire source. The
developed real time fire detector model was tested in real time using lighter
as fire source. In the test results; the 99% of accuracy was obtained using
developed Faster R-CNN fire detector model.

Keywords

Faster R-CNN, Loss Function, Real time Fire Detection, Tensorflow and Tensorboard

FASTER R-CNN MODELİ KULLANARAK GERÇEK ZAMANLI YANGIN ALGILAMA

Abstract

Bu çalışmanın amacı, Faster R-CNN kullanarak gerçek zamanlı bir yangın dedektörü geliştirmektir. Sistemin makine öğrenme süreci için; 1.000 resim (yangın ve yangın sahneleri dahil), eğitim ve doğrulama için sırasıyla %80 ve 20 oranında kullanılmıştır. Makine öğrenme işlemi, NVidia GeForce GTX 1070 Ti'nin 17 GB dahili belleğe sahip özelliklerine sahip bir sistem kullanılarak gerçekleştirildi. Anaconda sanal ortamına gerekli yazılımlar (Python 3.5, Tensorflow 1.13.1, OpenCV, CUDA-cuDNN araç setleri) kuruldu. Görüntülerdeki yangın sahneleri, LabelImg yazılımı kullanılarak yangın ve yangın olmayan olarak etiketlendi. Eğitim sürecinin ölçümleri Tensorboard'dan alınmıştır. Eğitimde 40.000'lik adımlarla toplam zarar değeri 2'den 0,02'ye düşürüldü. Lost fonksiyonu 0.05 seviyesinden düşük olduğu için, çıkarım grafiği donmuş ve yangın kaynağını tespit etmek için dışa aktarılmıştır. Geliştirilen gerçek zamanlı yangın dedektörü modeli, yangın kaynağı olarak çakmak kullanılarak gerçek zamanlı olarak test edildi. Test sonuçlarında; doğruluğun % 99'u gelişmiş Faster R-CNN yangın dedektörü modeli kullanılarak elde edildi.

Keywords

Faster R-CNN, Loss Function, Real time Fire Detection, Tensorflow and Tensorboard

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