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In order to monitor and detect fire in dense population areas close to forests such as easy adoption of certain fire detection system on campus, the existing methods often struggle to meet the demands of large-scale data acquisition and analytical processing, which are critical for effective fire detection and response in urban environments.
- Our Fire and Smoke Dataset
- The 8-Scenes Dataset
- Project Structure
- Preparing Our Fire and Non-Fire Combined Dataset
- Our Configuration File
- Implementing Our Fire Detection Convolutional Neural Network
- Creating Our Training Script
- Training The Fire Detection Model with Keras
- Making Predictions on Fire/Non-Fire Images
- Fire Detection Results
The dataset we’ll be using for fire and smoke examples was curated by PyImageSearch reader, Gautam Kumar. Guatam gathered a total of 1,315 images by searching Google Images for queries related to the term “fire”, “smoke”, etc. However, the original dataset has not been cleansed of extraneous, irrelevant images that are not related to fire and smoke...
The dataset we’ll be using for Non-fire examples is called 8-scenes as it contains 2,688 imageexamples belonging to eight natural scene categories (all without fire): 1. Coast 2. Mountain 3. Forest 4. Open country 5. Street 6. Inside city 7. Tall buildings 8. Highways The dataset was originally curated by Oliva and Torralba in their 2001 paper, Mod...
Go ahead and grab today’s .zip from the source code and pre-trained model using the “Downloads”section of this blog post. From there you can unzip it on your machine and your project will look like Figure 4. There is an exception: neither dataset .zip (white arrows) will be present yet. We will download, extract, and prune the datasets in the next ...
Preparing our Fire and Non-firedataset involves a four-step process: 1. Step #1: Ensure you followed the instructions in the previous section to grab and unzip today’s files from the “Downloads”section. 2. Step #2: Download and extract the fire/smoke dataset into the project. 3. Step #3:Prune the fire/smoke dataset for extraneous, irrelevant files....
This project will span multiple Python files that will need to be executed, so let’s store all important variables in a single config.pyfile. Open up config.pynow and insert the following code: We’ll use the os module for combining paths (Line 2). Lines 5-7 contain paths to our (1) Fire images, and (2) Non-fireimages. Line 10is a list of our two cl...
In this section we’ll implement FireDetectionNet, a Convolutional Neural Network used to detect smoke and fire in images. This network utilizes depthwise separable convolutionrather than standard convolution as depthwise separable convolution: 1. Is more efficient,as Edge/IoT devices will have limited CPU and power draw. 2. Requires less memory,as ...
Our training script will be responsible for: 1. Loading our Fire and Non-firecombined dataset from disk. 2. Instantiating our FireDetectionNetarchitecture. 3. Finding our optimal learning rate by using our LearningRateFinderclass. 4. Taking the optimal learning rate and training our network for the full set of epochs. Let’s get started! Open up the...
Training our fire detection model is broken down into three steps: 1. Step #1: Run the train.py script with the --lr-findcommand line argument to find our optimal learning rate. 2. Step #2: Update Line 17 of our configuration file (config.py ) to set our INIT_LRvalue as the optimal learning rate. 3. Step #3: Execute the train.pyscript again, but th...
Given our trained fire detection model, let’s now learn how to: 1. Load the trained model from disk. 2. Sample random images from our dataset. 3. Classify each input image using our model. Open up predict_fire.pyand insert the following code: Lines 2-9 handle our imports, namely load_model, so that we can load our serialized TensorFlow/Keras model ...
To see our fire detector in action make sure you use the “Downloads”section of this tutorial to download the source code and pre-trained model. From there you can execute the following command: I’ve included a set sample of results in Figure 8 — notice how our model was able to correctly predict “fire” and “non-fire”in each of them.
Jan 15, 2024 · The system consists of three main components, which are motion detection, visual saliency detection (VSD), and classification of fire images using transfer learning methodology. In order to effectively retrieve the relevant object of interest, the authors only applied the VSD algorithm to the maps that contained moving objects by using ResNet-18 as the backbone.
Nov 17, 2023 · Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning ...
Fire alarm systems designed with Deep Learning algorithms are more sophisticated than traditional fire alarm systems in terms of saving lives. Isolated sensors have traditionally been used to detect fires, but they are incapable of determining the extent of the fire and informing disaster preparedness teams. To address this vulnerability, this research presents an intelligent fire detection ...
5 days ago · Extreme catastrophes can be experienced if fire is not put out in time. This urges us to come up with fire detection systems that trigger an alarm while the fire is still in its early stages. Therefore, in this project we used deep learning approach. The designed model is a relatively cheaper fire detection CNN architecture for cctv cameras.
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Aug 31, 2023 · Fire alarm detection systems play a crucial role [20] in preventing more severe fires and minimizing their impact on the environment and human life [11]. IT professionals and researchers have ...
