Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network

Sheng, Dali; Deng, Jiankang; Xiang, Jiawei

doi:10.1109/access.2021.3075731

Cited by 22 publications

(4 citation statements)

References 43 publications

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“…Consequently, the security of the transmission line corridor is frequently compromised. The destruction of transmission line is caused not only by fires but also by various other factors (Gu, et al, 2020;Sheng et al, 2021). Additionally, the presence of large tower cranes and engineering machinery also poses a significant risk.…”

Section: Introductionmentioning

confidence: 99%

Research on detection of transmission line corridor external force object containing random feature targets

Zou,

Ye,

Sun

et al. 2024

Front. Energy Res.

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With the objective of achieving “double carbon,” the power grid is placing greater importance on the security of transmission lines. The transmission line corridor has complex situations with external force targets and irregularly featured objects including smoke. For this reason, in this paper, the high-performance YOLOX-S model is selected for transmission line corridor external force object detection and improved to enhance model multi-object detection capability and irregular feature extraction capability. Firstly, to enhance the perception capability of external force objects in complex environment, we improve the feature output capability by adding the global context block after the output of the backbone. Then, we integrate convolutional block attention module into the feature fusion operation to enhance the recognition of objects with random features, among the external force targets by incorporating attention mechanism. Finally, we utilize EIoU to enhance the accuracy of object detection boxes, enabling the successful detection of external force targets in transmission line corridors. Through training and validating the model with the established external force dataset, the improved model demonstrates the capability to successfully detect external force objects and achieves favorable results in multi-class target detection. While there is improvement in the detection capability of external force objects with random features, the results indicate the need to enhance smoke recognition, particularly in further distinguishing targets between smoke and fog.

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Section: Introductionmentioning

confidence: 99%

Research on detection of transmission line corridor external force object containing random feature targets

Zou,

Ye,

Sun

et al. 2024

Front. Energy Res.

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“…Although all of the above achieved good results, there are still problems in existing research on smoke detection. Sheng, D. et al [16] used a CNN network and linear iterative clustering (SLIC) for smoke image segmentation and applied density-based spatial clustering of applications with noise (DBSCAN), which can achieve faster detection. However, their proposed method has a low FPR rate, which indicates high model sensitivity and needs further improvement.…”

Section: Introductionmentioning

confidence: 99%

LMDFS: A Lightweight Model for Detecting Forest Fire Smoke in UAV Images Based on YOLOv7

Chen,

Cheng,

Lin

et al. 2023

Remote Sensing

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Forest fires pose significant hazards to ecological environments and economic society. The detection of forest fire smoke can provide crucial information for the suppression of early fires. Previous detection models based on deep learning have been limited in detecting small smoke and smoke with smoke-like interference. In this paper, we propose a lightweight model for forest fire smoke detection that is suitable for UAVs. Firstly, a smoke dataset is created from a combination of forest smoke photos obtained through web crawling and enhanced photos generated by using the method of synthesizing smoke. Secondly, the GSELAN and GSSPPFCSPC modules are built based on Ghost Shuffle Convolution (GSConv), which efficiently reduces the number of parameters in the model and accelerates its convergence speed. Next, to address the problem of indistinguishable feature boundaries between clouds and smoke, we integrate coordinate attention (CA) into the YOLO feature extraction network to strengthen the extraction of smoke features and attenuate the background information. Additionally, we use Content-Aware Reassembly of FEatures (CARAFE) upsampling to expand the receptive field in the feature fusion network and fully exploit the semantic information. Finally, we adopt SCYLLA-Intersection over Union (SIoU) loss as a replacement for the original loss function in the prediction phase. This substitution leads to improved convergence efficiency and faster convergence. The experimental results demonstrate that the LMDFS model proposed for smoke detection achieves an accuracy of 80.2% with a 5.9% improvement compared to the baseline and a high number of Frames Per Second (FPS)—63.4. The model also reduces the parameter count by 14% and Giga FLoating-point Operations Per second (GFLOPs) by 6%. These results suggest that the proposed model can achieve a high accuracy while requiring fewer computational resources, making it a promising approach for practical deployment in applications for detecting smoke.

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“…Kristianto et al (2020 used land surface temperature (LST) data and local agency statistics to cluster the local fire high-risk areas. Sheng et al (2021) presented a method based on DBSCAN and convolutional neural network to recognise flame and smoke modes connected to fire stages. Vatresia et al (2020) proposed a spatio-temporal clustering method with DBSCAN to cluster hotspot data over Sulawesi Island from 2016 to 2018.…”

Section: Introductionmentioning

confidence: 99%

An improved spatio-temporal clustering method for extracting fire footprints based on MCD64A1 in the Daxing’anling Area of north-eastern China

2023

Int. J. Wildland Fire

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Background. Understanding the spatio-temporal dynamics associated with a wildfire event is essential for projecting a clear profile of its potential ecological influences. Aims. To develop a reliable framework to extract fire footprints from MODIS-based burn products to facilitate the understanding of fire event evolution. Methods. This study integrated the Jenks natural breaks classification method and the density-based spatial clustering of applications with noise (DBSCAN) algorithm to extract the fire footprints in Daxing'anling region of China between 2001 and 2006 from MCD64A1 burned area data. Key results. The results showed that the fire footprints extracted by the model gained an overall accuracy of 80% in spatial and temporal domains after an intensive validation by using the historical fire records provided by the local agency. The agreement of burned area between the extracted fire patches and the historical fire records for those matched fire points was characterised by an overall determination coefficient R 2 at 0.91. Conclusions. The proposed framework serves as an efficient and convenient wildfire management tool for areas requiring large-scale and long-term wildfire monitoring. Implications. The current framework can be used to create a reliable large-scale fire event database by providing an important alternative for the improvement of field investigation.

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Automatic Smoke Detection Based on SLIC-DBSCAN Enhanced Convolutional Neural Network

Cited by 22 publications

References 43 publications

Research on detection of transmission line corridor external force object containing random feature targets

Research on detection of transmission line corridor external force object containing random feature targets

LMDFS: A Lightweight Model for Detecting Forest Fire Smoke in UAV Images Based on YOLOv7

An improved spatio-temporal clustering method for extracting fire footprints based on MCD64A1 in the Daxing’anling Area of north-eastern China

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