Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

Liau, Hengfui; Yamini, Nimmagadda; YengLiong, Wong,

doi:10.48550/arxiv.1806.05363

Cited by 2 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, both low-light scenarios, such as underground settlements and typical situations, were collected, and the model was learned using 10,200 target data points and 14,850 supporting data points. Liau et al [18] developed a FireSSD model based on a single shot detector (SSD) appropriate for edge devices. They added Residual connection and group convolution to the SqueezeNet-based SSD model and used the Wide Fire module, Dynamic Mbox detection layer, normalization, and Dropout modules to improve the accuracy while maintaining real-time performance in the CPU.…”

Section: Introductionmentioning

confidence: 99%

Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins

Kim,

Lam,

Lee

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Fire is amorphous and occurs differently depending on the space, environment, and material of the fire. In particular, the early detection of fires is a very important task in preventing large-scale accidents; however, there are currently almost no learnable early fire datasets for machine learning. This paper proposes an early fire detection system optimized for certain spaces using a digital-twin-based automatic fire learning data generation model for each space. The proposed method first automatically generates realistic particle-simulation-based synthetic fire data on an RGB-D image matched to the view angle of a monitoring camera to build a digital twin environment of the real space. In other words, our method generates synthetic fire data according to various fire situations in each specific space and then performs transfer learning using a state-of-the-art detection model with these datasets and distributes them to AIoT devices in the real space. Synthetic fire data generation optimized for a space can increase the accuracy and reduce the false detection rate of existing fire detection models that are not adaptive to space.

show abstract

Section: Introductionmentioning

confidence: 99%

Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins

Kim,

Lam,

Lee

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…For example, Zhang et al [6] added a lightweight attention module to the CSPLayer layer of YOLOX to improve the overall fire detection performance of the model. HengFui et al [7] replaced the SSD's backend network with a more efficient SqueezeNet to improve the accuracy and speed of fire detection. However, in the face of the complex and changeable real environment, these fire detection models still have some problems.…”

Section: Introductionmentioning

confidence: 99%

ES-YOLO: a new lightweight fire detection model

Wang

Wang²

2023

Third International Conference on Computer Vision and Data Mining (ICCVDM 2022)

View full text Add to dashboard Cite

For the problems of fire detection models based on computer vision, such as long inference and training time, too many model parameters and low detection accuracy. We propose ES-YOLO, which can quickly and accurately detect flames and smoke. Firstly, the original YOLOv5s backbone network is replaced with EfficientNetV2, which reduces the computational complexity of the network and improves the detection accuracy. Secondly, replaces the CIoU loss function with SIoU, which speeds up the convergence of the model. Finally, 9-Mosaic data augmentation is designed to enrich the dataset. The experimental results on the PASCAL VOC2007 dataset demonstrate that the mAP@0.5 and recall of ES-YOLO are 20% and 15% higher than that of YOLOv5s, the size of the model are compressed to 1/2 of that of YOLOv5s. ES-YOLO meets the requirements of lightweight and real-time detection.

show abstract

Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device

Cited by 2 publications

References 0 publications

Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins

Early Fire Detection System by Using Automatic Synthetic Dataset Generation Model Based on Digital Twins

ES-YOLO: a new lightweight fire detection model

Contact Info

Product

Resources

About