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"fire detection algorithm" » "time detection algorithm" (Expand Search), "false detection algorithm" (Expand Search), "case detection algorithm" (Expand Search)
"neural coding algorithm" » "neural cosine algorithm" (Expand Search), "neural modeling algorithm" (Expand Search), "neural finding algorithm" (Expand Search)
"element data algorithms" » "element art algorithms" (Expand Search), "element all algorithms" (Expand Search), "element both algorithms" (Expand Search), "settlement data algorithms" (Expand Search), "relevant data algorithms" (Expand Search), "movement data algorithms" (Expand Search)
"fire detection algorithm" » "time detection algorithm" (Expand Search), "false detection algorithm" (Expand Search), "case detection algorithm" (Expand Search)
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1
The principle of Partial Convolution.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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2
Ablation experiments results of YOLOv5s.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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3
Overall network architecture of FCMI-YOLO.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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4
The principle of MLCA mechanism.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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5
Parameters of the dataset.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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6
Comparison of mAP@0.5 for different ratios.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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7
Primary training parameters for the model.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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8
Distribution of the dataset.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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9
Parameters of the FasterNext and C3.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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10
Performance comparison of mainstream algorithms.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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11
System diagram.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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12
Schematic diagram of Inner-IoU.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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13
Model train environment.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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14
The structure of FasterNext.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”
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15
The structure of MLCA mechanism.
Published 2025“…<div><p>The rapid development of Internet of Things (IoT) technology and deep learning has propelled the deployment of vision-based fire detection algorithms on edge devices, significantly exacerbating the trade-off between accuracy and inference speed under hardware resource constraints. …”