FIRE-DET: 一种高效的火焰检测模型
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淮阴工学院计算机与软件工程学院

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国家重点研发计划项目(2018YFB1004904);江苏省高校自然科学研究重大项目(18KJA520001);2021年淮阴工学院研究生科技创新计划项目(HGYK202122)


FIRE-DET: An efficient flame detection model
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HYIT,College of Computer and Software Engineering

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    摘要:

    模型的效率在计算机视觉中变得越来越重要。本文通过研究用于火焰检测的神经网络结构,提出了几个关键的优化方案,以提高模型效率和检测效果。第一,提出一种由多卷积组合结构构建的主干网络(FIRE-Net),它能高效的从多个尺度上提取丰富的火焰特征;第二,提出一种改进的加权双向特征金字塔网络(BiFPN-mini)以快速地实现多尺度特征融合;第三,提出一种新的注意力机制(FIRE-Attention),让检测器对火焰特征更敏感。基于上述优化,本文开发出了一种全新的火焰检测器FIRE-DET,它在硬件资源有限的条件下能够取得比现有基于深度学习的火焰检测方法更快的检测效率。FIRE-DET模型在自建数据集上进行训练后,最终对火焰检测的准确率和帧率分别达到97%和85FPS。实验结果表明,与主流算法相比,本文火焰检测模型检测性能更优。本文为解决火焰探测问题提供了一个更通用的解决方案.

    Abstract:

    The efficiency of models is becoming more and more important in computer vision. In this paper, by studying the neural network structure for flame detection, several key optimization schemes are proposed to improve the efficiency of the model and the detection effect. First, this paper proposes a backbone network (FIRE-Net) constructed from a multi-convolution combined structure, which can efficiently extract rich flame features from multiple scales; second, by using an improved weighted bidirectional feature Pyramid network (BiFPN-mini) to quickly achieve multi-scale feature fusion; third, a new attention mechanism (FIRE-Attention) is proposed to make the detector more sensitive to flame characteristics. based on the above optimization , Developed a new flame detector FIRE-DET, which can always achieve faster detection efficiency than existing deep learning-based detection methods under the condition of limited hardware resources. The FIRE-DET model was trained on a self-built dataset, and the final flame recognition accuracy and frame rate reached 97% and 85FPS, respectively. The experimental results show that this flame detection model detection performance is better than the mainstream algorithm. This article provides a more common solution to solve the problem of flame detection.

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  • 收稿日期:2021-11-12
  • 最后修改日期:2021-12-15
  • 录用日期:2021-12-17
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