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一种基于DexiNed改进的红外图像边缘检测算法

何谦 刘伯运

何谦, 刘伯运. 一种基于DexiNed改进的红外图像边缘检测算法[J]. 红外技术, 2021, 43(9): 876-884.
引用本文: 何谦, 刘伯运. 一种基于DexiNed改进的红外图像边缘检测算法[J]. 红外技术, 2021, 43(9): 876-884.
HE Qian, LIU Boyun. Improved Infrared Image Edge Detection Algorithm Based on DexiNed[J]. Infrared Technology , 2021, 43(9): 876-884.
Citation: HE Qian, LIU Boyun. Improved Infrared Image Edge Detection Algorithm Based on DexiNed[J]. Infrared Technology , 2021, 43(9): 876-884.

一种基于DexiNed改进的红外图像边缘检测算法

详细信息
    作者简介:

    何谦(1998-), 男, 硕士研究生, 研究方向为图像处理。E-mail:1072633933@qq.com

  • 中图分类号: TP391.4

Improved Infrared Image Edge Detection Algorithm Based on DexiNed

  • 摘要: 相对于可见光图像边缘检测,目前针对红外图像边缘检测的研究较少,且大多基于传统方法,如边缘检测算子、数学形态学等,其本质上都是只考虑红外图像局部的急剧变化来检测边缘,因而始终受限于低层次特征。本文提出了一种基于深度学习的红外图像边缘检测算法,在DexiNed(Dense Extreme Inception Network for Edge Detection)的基础上,缩减了网络规模,并在损失函数中引入了图像级的差异,精心设置了损失函数的参数,进而优化了网络性能。此外,还通过调整自然图像边缘检测数据集来近似模拟红外图像边缘检测数据集,对改进后的模型进行训练,进一步提高了网络对红外图像中边缘信息的提取能力。定性评价结果表明,本文方法提取的红外图像边缘定位准确、层次清晰、细节丰富、贴合人眼视觉,使用了SSIM(Structural Similarity Index Measure)和FSIM(Feature Similarity Index Measure)指标的定量评价结果进一步体现了本文方法相比于其他方法的优越性。
  • 图  1  几种图像边缘检测方法提取红外图像边缘的效果对比:(a)红外图像(来源于FLIR红外数据集);(b)Canny算子的边缘检测结果;(c)BDCN[12]的边缘检测结果;(d)本文方法的边缘检测结果

    Figure  1.  Comparison of several image edge detection methods to extract infrared image edge: (a) is an example infrared image from FLIR Thermal Dataset (www.flir.com); (b) is the result of the Canny edge detector; (c) is the result of BDCN[12]; (d) is the result of our method

    图  2  DexiNed网络结构和精简后的网络(位于虚线框中)

    Figure  2.  Network architecture of DexiNed[14] and simplified one(in dotted box)

    图  3  可见光图像转变为模拟红外图像的过程

    Figure  3.  The process of transforming an optical image into a simulated infrared image

    图  4  测试结果对比

    Figure  4.  Comparison of test results

    Infrared image Result 1 Result 2 Result 3

    图  5  在IR-BIPED数据集上训练前后的网络测试结果对比

    Figure  5.  Network test before and after training on IR-BIPED dataset

    图  6  不同边缘检测方法的结果对比

    Figure  6.  Results of different methods

    Image-1 Image-2 Image-3 Image-4 Infrared image Enhanced Canny[3] Improved ant colony [5] RCF[11] BDCN[12] Our method

    图  7  不同方法在图像1上测试后的定量结果对比

    Figure  7.  Comparison of quantitative results of different methods on image1

    图  8  不同方法在图像2上测试后的定量结果对比

    Figure  8.  Comparison of quantitative results of different methods on image2

    图  9  不同方法在图像3上测试后的定量结果对比

    Figure  9.  Comparison of quantitative results of different methods on image3

    图  10  不同方法在图像4上测试后的定量结果对比

    Figure  10.  Comparison of quantitative results of different methods on image4

    表  1  网络准确度对比1

    Table  1.   Comparison of network accuracy-1

    Baseline Simplified
    epoch 1 0.8991782 0.8991683
    epoch 2 0.8993612 0.8995107
    epoch 3 0.8993013 0.8994964
    下载: 导出CSV

    表  2  网络准确度对比2

    Table  2.   Comparison of network accuracy-2

    Baseline Simplified+designed loss
    epoch 1 0.8991875 0.9012373
    epoch 2 0.8991702 0.9105164
    epoch 3 0.8992519 0.9097796
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-23
  • 修回日期:  2021-04-16
  • 刊出日期:  2021-09-20

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