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Volume 45 Issue 5
May  2023
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ZHOU Jinjie, JI Li, ZHANG Qian, ZHANG Baohui, YUAN Xilin, LIU Yanqing, YUE Jiang. Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm[J]. Infrared Technology , 2023, 45(5): 506-512.
Citation: ZHOU Jinjie, JI Li, ZHANG Qian, ZHANG Baohui, YUAN Xilin, LIU Yanqing, YUE Jiang. Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm[J]. Infrared Technology , 2023, 45(5): 506-512.
shu

Multiscale Infrared Object Detection Network Based on YOLO-MIR Algorithm

  • 1.

    Nanjing Research Center, Kunming Institute of Physics, kunming 650221, China

  • 2.

    College of Science, Hohai University, Nanjing 210024, China

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  • Received Date: February 05, 2023
  • Revised Date: March 30, 2023
    Graphical Abstract
    • Abstract
  • To address the low detection accuracy and poor robustness of infrared images compared with visible images, a multiscale object detection network YOLO-MIR(YOLO for multiscale IR images) for infrared images is proposed. First, to increase the adaptability of the network to infrared images, the feature extraction and fusion modules were improved to retain more details in the infrared images. Second, the detection ability of multiscale objects is enhanced, the scale of the fusion network is increased, and the fusion of infrared image features is facilitated. Finally, a data augmentation algorithm for infrared images was designed to increase the network robustness. Ablation experiments were conducted to evaluate the impact of different methods on the network performance, and the results show that the network performance was significantly improved using the infrared dataset. Compared with the prevalent algorithm YOLOv7, the average detection accuracy of this algorithm was improved by 3%, the adaptive ability to infrared images was improved, and the accurate detection of targets at various scales was realized.
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    • References (21)
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