Fire detection based on video processing method

Nguyen-Ti, Ti; Nguyen-Phuc, Thuan; Do-Hong, Tuan

doi:10.1109/atc.2013.6698087

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…In many applications, smoke detection can greatly reduce the damages associated with fire. Traditional smoke detection techniques are usually based on thermal sensing or particle concentration estimation [1], but these detectors are ineffective in open spaces. Visual smoke detection technologies [2,3] use computer vision to monitor and locate smoke from images, so they are more suitable for smoke detection in open and wild scenes than traditional sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by the successes of FCN-based methods, there are some smoke segmentation approaches based on full convolutional neural networks that have been proposed. Existing smoke segmentation methods also encounter two common challenges: (1) one difficulty in capturing contextual information and spatial details at the same time, and (2) another difficulty in handling the varied shapes and textures of smoke. The first one is the contradiction between local spatial details and global semantic information.…”

Section: Introductionmentioning

confidence: 99%

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A dense multi‐scale context and asymmetric pooling embedding network for smoke segmentation

Wen,

Zhou,

et al. 2023

IET Computer Vision

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It is very challenging to accurately segment smoke images because smoke has some adverse vision characteristics, such as anomalous shapes, blurred edges, and translucency. Existing methods cannot fully focus on the texture details of anomalous shapes and blurred edges simultaneously. To solve these problems, a Dense Multi‐scale context and Asymmetric pooling Embedding Network (DMAENet) is proposed to model the smoke edge details and anomalous shapes for smoke segmentation. To capture the feature information from different scales, a Dense Multi‐scale Context Module (DMCM) is proposed to further enhance the feature representation capability of our network under the help of asymmetric convolutions. To efficiently extract features for long‐shaped objects, the authors use asymmetric pooling to propose an Asymmetric Pooling Enhancement Module (APEM). The vertical and horizontal pooling methods are responsible for enhancing features of irregular objects. Finally, a Feature Fusion Module (FFM) is designed, which accepts three inputs for improving performance. Low and high‐level features are fused by pixel‐wise summing, and then the summed feature maps are further enhanced in an attention manner. Experimental results on synthetic and real smoke datasets validate that all these modules can improve performance, and the proposed DMAENet obviously outperforms existing state‐of‐the‐art methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%