Indonesia's Forest Fire and Haze Pollution: An Analysis of Human Security

Fachrie, Muhammad

doi:10.22452/mjir.vol8no1.7

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…The experimental results show that collage augmentation is beneficial in balancing the proportion of small targets and improving the training effect. It can be seen that when k = 2 2 , the model has optimal accuracy and the speed is not significantly reduced compared to k = 1 2 , indicating that component stitching data enhancement can alleviate the imbalance of the dataset due to the under-representation of small targets. Therefore, PDAM-STPNNet uses the component stitching data enhancement approach set to k = 2 2 .…”

Section: Exploring the Effects Of A Single Approachmentioning

confidence: 99%

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PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

Zhan

Cai

et al. 2021

Symmetry

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The target detection of smoke through remote sensing images obtained by means of unmanned aerial vehicles (UAVs) can be effective for monitoring early forest fires. However, smoke targets in UAV images are often small and difficult to detect accurately. In this paper, we use YOLOX-L as a baseline and propose a forest smoke detection network based on the parallel spatial domain attention mechanism and a small-scale transformer feature pyramid network (PDAM–STPNNet). First, to enhance the proportion of small forest fire smoke targets in the dataset, we use component stitching data enhancement to generate small forest fire smoke target images in a scaled collage. Then, to fully extract the texture features of smoke, we propose a parallel spatial domain attention mechanism (PDAM) to consider the local and global textures of smoke with symmetry. Finally, we propose a small-scale transformer feature pyramid network (STPN), which uses the transformer encoder to replace all CSP_2 blocks in turn on top of YOLOX-L’s FPN, effectively improving the model’s ability to extract small-target smoke. We validated the effectiveness of our model with recourse to a home-made dataset, the Wildfire Observers and Smoke Recognition Homepage, and the Bowfire dataset. The experiments show that our method has a better detection capability than previous methods.

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Section: Exploring the Effects Of A Single Approachmentioning

confidence: 99%

“…Forest fires can cause widespread forest mortality, bringing huge losses to forest ecological resources and the social economy, and serious forest fires can even lead to human casualties [1,2]. In recent years, the frequency of forest fires has increased, and the extent of the damage has been increasing year by year.…”

Section: Introductionmentioning

confidence: 99%

PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

Zhan

Cai

et al. 2021

Symmetry

View full text Add to dashboard Cite

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Indonesia's Forest Fire and Haze Pollution: An Analysis of Human Security

Cited by 1 publication

References 13 publications

PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

PDAM–STPNNet: A Small Target Detection Approach for Wildland Fire Smoke through Remote Sensing Images

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