Image-Based Fire Detection Using Neural Networks

[[abstract]]An image-based fire detection method using neural networks is proposed in this paper. First, flame color features, based on the HSI color model, are trained by a backpropagation neural network for flame recognition. Then, based on the learned flame color features, regions with fire-like colors are roughly separated from an image. Besides segmenting flame regions, background objects with similar fire colors or resulted from the reflection of fire flames are also separated from the image. In order to get rid of these spurious fire-like regions, the image difference method and the invented color masking technique are applied. Finally, a compact method is devised to estimate the burning degree of fire flames so that users could be informed with a proper warning alarm. The proposed system can achieve 96.47% fire detection rate on average.[[sponsorship]]高雄應用科技大學 JCIS[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20061008~20061011[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]高雄, 臺

An Efficient Model for Forest Fire Detection using Deep Convolutional Neural Networks

Forest fires are a significant natural disaster that causes extensive damage to both human and wildlife habitats. Early detection and management of forest fires are critical in preventing potential losses. In recent years, deep learning-based approaches have emerged as promising solutions for forest fire detection. This paper proposes a deep learning-based approach for forest fire detection using SqueezeNet model.The proposed approach utilizes still images captured from forest areas under different weather conditions to classify whether an image contains a fire or not. The models were trained and tested using accuracy, precision, and recall metrics. The experimental results show that SqueezeNet achieve high precision, and recall in detecting forest fires.SqueezeNet is a Convolutional Neural Networks (CNN) architecture designed to reduce the number of parameters and computations required in a deep learning model while maintaining high accuracy in image classification tasks.

Bronco Ember An Edge Computing Acceleration Platform with Computer Vision

Bronco Ember is a nascent wildfire detection system that leverages edge computing capabilities, multi-spectral imaging, and artificial intelligence to greatly increase the performance of small satellite remote sensing payloads. The core hardware onboard is a SWIR InGaAs camera imaging in the 900nm to 1700nm wavelength and a GPU enabled single board computer. Artificial intelligence is used for fire detection and analysis using computer vision and neural networks being able to detect fires only filling a few pixels in each image. The system is based on traditional CNN networks and includes time series analysis that gives the system an 85% success rate in being able to detect wildfires with about a 50m diameter from a high-altitude balloon technology demonstration flight. The neural net is trained to monitor the movement and spread of the fire compared to prediction maps. This greatly reduces the number of false positive detected. The development of this payload has been supported through the NASA TechLeap Autonomous Observation Challenge No. 1 that has pushed the technology from concept to test flight in less than one calendar year. The system acts a rapid response remote sensing technology

Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing demands for computing and power resources has become inevitable. Spiking neural networks (SNNs) have attracted widespread interest as the third-generation of neural networks due to their event-driven and low-powered nature. SNNs, however, are difficult to train, mainly owing to their complex dynamics of neurons and non-differentiable spike operations. Furthermore, their applications have been limited to relatively simple tasks such as image classification. In this study, we investigate the performance degradation of SNNs in a more challenging regression problem (i.e., object detection). Through our in-depth analysis, we introduce two novel methods: channel-wise normalization and signed neuron with imbalanced threshold, both of which provide fast and accurate information transmission for deep SNNs. Consequently, we present a first spiked-based object detection model, called Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic chip consumes approximately 280 times less energy than Tiny YOLO and converges 2.3 to 4 times faster than previous SNN conversion methods.Comment: Accepted to AAAI 202