Hazardous incidences have significant influences on human life,and fire is one of the foremost causes of such hazard in most nations.Fire prediction and classification model from a set of fire images can decrease the ...Hazardous incidences have significant influences on human life,and fire is one of the foremost causes of such hazard in most nations.Fire prediction and classification model from a set of fire images can decrease the risk of losing human lives and assets.Timely promotion of fire emergency can be of great aid.Therefore,construction of these prediction models is relevant and critical.This article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time.A fire image database is improved to enhance the images quality prior to classify them as either fire or nonfire.Our proposed deep learning-based Very Deep Convolutional Networks Visual Geometry Group(VGG-16)model(Parallel VGG-16)is an enhanced version of the VGG-16 model,by incorporating parallel convolution layers and a fusion module for better accuracy.The experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%,compared to the compared state-of-the-art models.Moreover,we integrate the prediction module into a UDOO BOLT V8 computer,which precisely controlled the fire alarm so that it can cautious people from fire in real time.In this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded system.Our experiments validate the effectiveness and applicability of the proposed fire model.展开更多
Forest fire prediction constitutes a significant component of forestmanagement. Timely and accurate forest fire prediction will greatly reduce property andnatural losses. A quick method to estimate forest fire hazard ...Forest fire prediction constitutes a significant component of forestmanagement. Timely and accurate forest fire prediction will greatly reduce property andnatural losses. A quick method to estimate forest fire hazard levels through knownclimatic conditions could make an effective improvement in forest fire prediction. Thispaper presents a description and analysis of a forest fire prediction methods based onmachine learning, which adopts WSN (Wireless Sensor Networks) technology andperceptron algorithms to provide a reliable and rapid detection of potential forest fire.Weather data are gathered by sensors, and then forwarded to the server, where a firehazard index can be calculated.展开更多
文摘Hazardous incidences have significant influences on human life,and fire is one of the foremost causes of such hazard in most nations.Fire prediction and classification model from a set of fire images can decrease the risk of losing human lives and assets.Timely promotion of fire emergency can be of great aid.Therefore,construction of these prediction models is relevant and critical.This article proposes an operative fire prediction model that depends on a prediction unit embedded in the processor UDOO BOLT V8 hardware to predict fires in real time.A fire image database is improved to enhance the images quality prior to classify them as either fire or nonfire.Our proposed deep learning-based Very Deep Convolutional Networks Visual Geometry Group(VGG-16)model(Parallel VGG-16)is an enhanced version of the VGG-16 model,by incorporating parallel convolution layers and a fusion module for better accuracy.The experimental results validate the performance of the Parallel VGG-16 which achieves an accuracy of 97%,compared to the compared state-of-the-art models.Moreover,we integrate the prediction module into a UDOO BOLT V8 computer,which precisely controlled the fire alarm so that it can cautious people from fire in real time.In this paper we propose a complete fire prediction model using a camera and the UDOO BOLT V8 embedded system.Our experiments validate the effectiveness and applicability of the proposed fire model.
文摘Forest fire prediction constitutes a significant component of forestmanagement. Timely and accurate forest fire prediction will greatly reduce property andnatural losses. A quick method to estimate forest fire hazard levels through knownclimatic conditions could make an effective improvement in forest fire prediction. Thispaper presents a description and analysis of a forest fire prediction methods based onmachine learning, which adopts WSN (Wireless Sensor Networks) technology andperceptron algorithms to provide a reliable and rapid detection of potential forest fire.Weather data are gathered by sensors, and then forwarded to the server, where a firehazard index can be calculated.
