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Volume 43 Issue 9
Sep.  2021
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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.
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Improved Infrared Image Edge Detection Algorithm Based on DexiNed

  • College of Power Engineering, Naval University of Engineering, Wuhan 430033, China

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  • Received Date: March 22, 2021
  • Revised Date: April 15, 2021
    Graphical Abstract
    • Abstract
  • Compared with optical image edge detection, there are fewer studies on infrared image edge detection, and most of them are based on traditional methods, such as edge detection operators and mathematical morphology. In essence, they only consider the sharp local changes of infrared images to detect edges, so they are always limited by low-level features. In this paper, an infrared image edge detection algorithm based on deep learning is proposed. Based on the dense extreme inception network for edge detection (DexiNed), the network capacity is reduced by removing the last main block, the image level difference is introduced into the loss function, and the parameters of the loss function are carefully set to optimize the network performance. In addition, by adjusting the natural image edge detection dataset to approximate the infrared image edge detection dataset, the improved model was trained to enhance the edge detection ability. The qualitative evaluation results show that the edge of the infrared image extracted by our method is accurate, precise, rich in detail, and fits human vision. A quantitative evaluation using the structural similarity indexmatrix (SSIM) and feature similarity indexmatrix (FSIM) indexes further shows the advantages of our method compared with other existing methods.
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    • References (26)
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