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Volume 44 Issue 5
May  2022
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XUN Huasheng, ZHANG Jingjing, LIU Xiao, LI Teng, NIAN Fudong, ZHANG Xin. Multi-Target Detection of Low-Illuminance Scene Based on Polarization Image[J]. Infrared Technology , 2022, 44(5): 483-491.
Citation: XUN Huasheng, ZHANG Jingjing, LIU Xiao, LI Teng, NIAN Fudong, ZHANG Xin. Multi-Target Detection of Low-Illuminance Scene Based on Polarization Image[J]. Infrared Technology , 2022, 44(5): 483-491.
shu

Multi-Target Detection of Low-Illuminance Scene Based on Polarization Image

  • 1.

    Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei 230601, China

  • 2.

    Anhui Province Key Laboratory of Polarization Detection Technology, Hefei 230031, China

  • 3.

    Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei 230031, China

  • 4.

    School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, China

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  • Received Date: June 14, 2021
  • Revised Date: August 02, 2021
    Graphical Abstract
    • Abstract
  • Polarized light reflection information can directly invert the intrinsic characteristics of a target and has strong anti-interference characteristics in the transmission process. Thus, polarization imaging technology can be applied to the fields of intelligent monitoring and traffic monitoring in various complex environments. In recent years, deep-neural-network methods for interpreting image detection targets have been developed rapidly and widely used in various fields of image processing. In this study, a vehicle multi-target detection algorithm based on polarized images and deep learning is proposed. First, the target polarization degree image can be obtained by acquiring the polarization image in real time and analyzing the polarization information. Second, to enhance the high contrast between the detection targets and the background in the polarization image, channel attention and spatial attention are introduced into the backbone network to improve the ability of the network features to perform adaptive learning. In addition, the K-means algorithm is used to perform clustering analysis on the target location information, thereby increasing the network's learning speed in the polarization image and improving the progress of target detection. The experimental results show that this method is effective and fast for vehicle detection in complex scenes with low illumination. This method combines the advantages of polarization imaging and deep-learning target detection and has substantial application scope in road vehicle target detection, recognition, and tracking.
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