• Infrared Technology
  • Vol. 44, Issue 11, 1154 (2022)
Ze WU, Xiaodong MIAO, Weiwen LI, and Hu YU
Author Affiliations
  • [in Chinese]
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    DOI: Cite this Article
    WU Ze, MIAO Xiaodong, LI Weiwen, YU Hu. Low-Visibility Road Target Detection Algorithm Based on Infrared and Visible Light Fusion[J]. Infrared Technology, 2022, 44(11): 1154 Copy Citation Text show less

    Abstract

    Both infrared and visible images are widely used in the field of target detection; however, unimodal images find it difficult to satisfy the requirements of low-visibility road target detection. Therefore, this study proposes a low-visibility road target detection algorithm based on infrared-visible fusion from the perspective of bimodal fusion. First, the input images were pre-processed using various IR-visible dual-mode image fusion algorithms, five parameters, including mean, standard deviation, information entropy, mean gradient, and spatial frequency of the fused images, were quantitatively analyzed, and the detection model for low-visibility road targets was obtained by optimizing the training detection network. Finally, the accuracies of the algorithm and model were evaluated in terms of the model-training results and target detection results. The experimental results demonstrate that the false- and missed-detection rates of the model trained by the algorithm in this study were significantly reduced compared with other algorithms, and the detection accuracy was improved from 75.51% to 88.86% compared with the existing algorithm using unimodal images; in addition, the image processing speed satisfied the requirement for real-time detection.
    WU Ze, MIAO Xiaodong, LI Weiwen, YU Hu. Low-Visibility Road Target Detection Algorithm Based on Infrared and Visible Light Fusion[J]. Infrared Technology, 2022, 44(11): 1154
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