ACOUSTIC EXTINGUISHING OF FLAMES DETECTED BY DEEP NEURAL NETWORKS IN EMBEDDED SYSTEMS

This paper presents the application of an intelligent flame and smoke detection platform based on artificial vision using Deep Neural Networks (DNN). An acoustic fire extinguisher can be connected to such a platform. In light of recent research, the use of acoustic technology is an environmentally friendly way of extinguishing flames. One of the advantages is then extinguishing immediately after positive detection of flames or smoke (without unnecessary time delay), which is a new combination in the field of fire protection. The authors contribution is the presentation of the conception of the intelligent acoustic extinguisher and the research work made to accomplish that including algorithms and tests. The main objective of the paper is to present the possibilities of acoustic extinguishing and fire detection using selected deep neural network models in embedded systems.

Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

Most of the existing smoke detection methods are based on manual operation, which is difficult to meet the needs of fire monitoring. To further improve the accuracy of smoke detection, an automatic feature extraction and classification method based on fast regional convolution neural network (fast R–CNN) was introduced in the study. This method uses a selective search algorithm to obtain the candidate images of the sample images. The preselected area coordinates and the sample image of visual task are used as network learning. During the training process, we use the feature migration method to avoid the lack of smoke data or limited data sources. Finally, a target detection model is obtained, which is strongly related to a specified visual task, and it has well-trained weight parameters. Experimental results show that this method not only improves the detection accuracy but also effectively reduces the false alarm rate. It can not only meet the real time and accuracy of fire detection but also realize effective fire detection. Compared with similar fire detection algorithms, the improved algorithm proposed in this paper has better robustness to fire detection and has better performance in accuracy and speed.

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.

A High-precision Forest Fire Smoke Detection Approach Based on DRGNet to Remote Sensing Through Uavs

The occurrence of forest fires can lead to ecological damage, property loss, and human casualties. Current forest fire smoke detection methods do not sufficiently consider the characteristics of smoke with high transparency and no clear edges and have low detection accuracy, which cannot meet the needs of complex aerial forest fire smoke detection tasks. In this paper, we propose Dual-ResNet50-vd with SoftPool based on a recursive feature pyramid with deconvolution and dilated convolution and global optimal nonmaximum suppression (DRGNet) for high-accuracy detection of forest fire smoke. First, the Dual-ResNet50-vd module is proposed to enhance the extraction of smoke features with high transparency and no clear edges, and SoftPool is used to retain more feature information of smoke. Then, a recursive feature pyramid with deconvolution and dilated convolution (RDDFPN) is proposed to fuse shallow visual features and deep semantic information in the channel dimension to improve the accuracy of long-range aerial smoke detection. Finally, global optimal nonmaximum suppression (GO-NMS) sets the objective function to globally optimize the selection of anchor frames to adapt to the aerial photography of multiple smoke locations in forest fire scenes. The experimental results show that the DRGNet parametric number on the UAV-IoT platform is as low as 53.48 M, mAP reaches 79.03%, mAP50 reaches 90.26%, mAP75 reaches 82.35%, FPS reaches 122.5, and GFLOPs reaches 55.78. Compared with other mainstream methods, it has the advantages of real-time detection and high accuracy.

Realization of People Density and Smoke Flow in Buildings during Fire Accidents Using Raspberry and OpenCV

Fire accidents in residential, commercial, and industrial environments are a major concern since they cause considerable infrastructure and human life damage. On other hand, the risk of fires is growing in conjunction with the growth of urban buildings. The existing techniques for detecting fire through smoke sensors are difficult in large regions. Furthermore, during fire accidents, the visibility of the evacuation path is occupied with smoke and, thus, causes challenges for people evacuating individuals from the building. To overcome this challenge, we have recommended a vision-based fire detection system. A vision-based fire detection system is implemented to identify fire events as well as to count the number people inside the building. In this study, deep neural network (DNN) models, i.e., MobileNet SSD and ResNet101, are embedded in the vision node along with the Kinect sensor in order to detect fire accidents and further count the number of people inside the building. A web application is developed and integrated with the vision node through a local server for visualizing the real-time events in the building related to the fire and people counting. Finally, a real-time experiment is performed to check the accuracy of the proposed system for smoke detection and people density.