Smoke and Fire Detection Base on Convolutional Neural Network

Wahyuni, Elvira Sukma; Hendri, Muhammad

doi:10.26760/elkomika.v7i3.455

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…CNN algorithm was a state-of-the-art method for identifying objects in images. Examples are provided by (Wahyuni & Hendri, 2019) to recognize smoke and fire and also (Dewi et al, 2019) to recognize various car types in the Intelligence Transportation System (ITS). Generally, object detection produced detection output in classes and bounding boxes.…”

Section: Rotatable Object Detectionmentioning

confidence: 99%

Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking

2023

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Tujuan dari penelitian ini adalah untuk mendeteksi objek dan mengestimasi pose objek menggunakan kamera RGB-D. Dalam penelitian ini, kami mengusulkan pemrosesan data pada citra RGB dan citra depth saja, tanpa menggunakan point cloud, seperti pada umumnya. Metode yang diusulkan mendeteksi posisi dan orientasi objek menggunakan DRBox-v2 dari Region of Interest (ROI), yang sebelumnya diperoleh dari pendeteksian pada penanda ArUco. Hasil deteksi objek kemudian diskalakan dan digunakan pada citra depth untuk mendapatkan perkiraan posisi dan orientasi objek. Dari sisi pendeteksi objek, usulan metode memperoleh nilai Average Precision (AP) sebesar 0,740. Sedangkan untuk estimator pose, usulan metode menghasilkan kesalahan posisi rata-rata 13,36 mm dan kesalahan orientasi rata-rata 0,75 derajat. Metode yang diusulkan berpotensi menjadi alternatif sistem deteksi objek dan estimasi pose pada kamera RGB-D yang tidak memerlukan pemrosesan point cloud dan tidak memerlukan model referensi objek.

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Section: Rotatable Object Detectionmentioning

confidence: 99%

Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking

2023

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“…In terms of image classification, CNN processes an input image prior to classifying it into a specific category [18]. CNN is a development of Multilayer Perceptron (MLP) intended to process twodimensional data [13]. Convolutional neural networks aim to process data that possess a network shape, such as topology.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…The pooling layer serves to reduce or reduce the number of parameters by using down-sampling operations to improve the positional invariance of a feature [13]. Based on Figure 4, the feature map obtains convolution results using a type of max pooling operation with a smaller size, namely a 2x2 matrix size [20].…”

Section: Pooling Layermentioning

confidence: 99%

Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern

Melinda,

Yunidar,

Andryani

2023

Green Intell. Syst. Appl.

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In the acquisition of amplitude data, the inaccuracy of a signal with the occurrence of an unstable peak value of the amplitude in the data is called a fluctuation. This study uses High-High Fluctuation (HHF) signal data from the acquisition of Multi-Spectral Capacitive Sensors (MSCS) with Hydrogen Dioxide (H2O) and Hydrogen Dioxide (H2O) objects mixed with Sodium Hydroxide (NaOH) that have been organized into a matrix form. The data acquisition results in previous studies have several parts that are difficult to distinguish with the naked eye. The method used in this study applies the CNN method for image recognition of signal fluctuations of type HHF from H2O and H2O mixed with NaOH, using the architecture known as AlexNet. Then, the H2O and H2O data groups mixed with NaOH were grouped into training data groups of 280 image data for each data type, and 70 image data for test data for both groups. During the training stage, the number of epochs used is 20. However, by the time the number of epochs reaches 15, the accuracy rate is already high, reaching 98%. Furthermore, at the testing stage, the CNN program can correctly recognize the entire 70 image data for both materials, achieving perfect recognition for the total amount of the two materials.

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“…In addition, the usage of video cameras to detect fire smoke was a frequently used method and not just limited to smoke sensors. Yet the system require high-resolution cameras and smoke images from these cameras were used to detect fire as reported in [9].…”

Section: -Illustration Of the Smoke From The Car Motorcycle And The F...mentioning

confidence: 99%

High Sensitivity Prototype Kit with IoT System for Fire Detection in a Peatland Swamp Forest Area

Hamzah,

Dalimin,

Md Som

et al. 2023

IJIE

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Smoke sensor of the photoelectric type is often used in indoor fire detection system. It is very sensitive to fire smoke and will trigger a fire alarm quickly when the presence of smoke is detected.By making a small innovation, this sensor can be implemented outdoors by means of IoT as used in the forest fire early detection system. However, the typical use of the sensor without any modification causes the sensitivity of the system to be very low such as failing to identify the actual presence of smoke. To increase the sensitivity of systems that use this type of sensor, the developed system is equipped withan automatic reclosing operation that is able to distinguish real smoke or not. The system has been designed to trigger cloud servers only after detecting smoke for ten seconds or beyond. System equipped with automatic reclosing operation show better response by not triggering cloud servers when detecting temporary smoke from car, motorcycle or nearby factory. When comparing the system before and after using this operation is, it was found that the system no longer sends smoke notification message to the user via Telegram channel when this temporary smoke is detected

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Smoke and Fire Detection Base on Convolutional Neural Network

Cited by 5 publications

References 5 publications

Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking

Object Detection and Pose Estimation with RGB-D Camera for Supporting Robotic Bin-Picking

Application of Convolutional Neural Network (CNN) Method in Fluctuations Pattern

High Sensitivity Prototype Kit with IoT System for Fire Detection in a Peatland Swamp Forest Area

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