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<p>Contactless detection of a target’s temperature of normal range (30&#xa0;°C ~ 150&#xa0;°C), which is based on its visible light images rather than infrared images, is a promising technology. Visible light imaging is primarily based on the visible light reflected from a target, rather than the thermal radiation of its own. The main challenge of contactless normal temperature detection through visible light images exists in the interference introduced by the variation of incident illumination. To solve this, a Retinex convolutional neural network (Retinex-CNN) was proposed in this paper, which was based on the convolutional neural network and Retinex algorithm. This network reduces the interference introduced by illumination variations and effectively improves the accuracy of the temperature detection based on visible light images. The temperature detection results of the Retinex-CNN shows that this network exhibits favorable generalization capability in terms of illumination variations, that is, it is still able to accurately detect temperature as a target is imaged in an illumination condition which is significantly different from that of the images in the training set. In this case, the Retinex-CNN obtains an average absolute error of 2.6&#xa0;°C, a value that is 6.89&#xa0;°C lower than that obtained by the CNN.</p>

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Realization of normal temperature detection through visible light images by Retinex-CNN

  • 期刊论文

  • 发表日期：

    2025年3月24日

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  • Jiayi Zhu,
  • Zhimin He,
  • Cheng Huang,
  • Jun Zeng,
  • Huichuan Lin,
  • Fuchang Chen,
  • Chaoqun Yu,
  • Yan Li,
  • Yongtao Zhang,
  • Huanting Chen,
  • Jixiong Pu

摘要

Contactless detection of a target’s temperature of normal range (30 °C ~ 150 °C), which is based on its visible light images rather than infrared images, is a promising technology. Visible light imaging is primarily based on the visible light reflected from a target, rather than the thermal radiation of its own. The main challenge of contactless normal temperature detection through visible light images exists in the interference introduced by the variation of incident illumination. To solve this, a Retinex convolutional neural network (Retinex-CNN) was proposed in this paper, which was based on the convolutional neural network and Retinex algorithm. This network reduces the interference introduced by illumination variations and effectively improves the accuracy of the temperature detection based on visible light images. The temperature detection results of the Retinex-CNN shows that this network exhibits favorable generalization capability in terms of illumination variations, that is, it is still able to accurately detect temperature as a target is imaged in an illumination condition which is significantly different from that of the images in the training set. In this case, the Retinex-CNN obtains an average absolute error of 2.6 °C, a value that is 6.89 °C lower than that obtained by the CNN.