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YOLO-HVS: Infrared Small Target Detection Inspired by the Human Visual System.

Xiaoge Wang1, Yunlong Sheng1, Qun Hao2

  • 1School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.

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Summary
This summary is machine-generated.

This study introduces YOLO-HVS, an infrared small target detection algorithm inspired by the human visual system. It enhances background suppression and multi-scale feature extraction for improved accuracy in challenging conditions.

Keywords:
YOLOv8attention mechanismhuman visual systeminfrared small target detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Infrared Imaging

Background:

  • Infrared small target detection faces challenges from background interference.
  • Limited multi-scale feature extraction hinders performance in complex scenarios.

Purpose of the Study:

  • To develop an efficient and robust algorithm for infrared small target detection.
  • To improve detection accuracy under severe occlusion and low signal-to-noise ratio (SNR) conditions.

Main Methods:

  • Proposes YOLO-HVS, an algorithm based on YOLOv8, incorporating a multi-scale spatially enhanced attention module (MultiSEAM).
  • Introduces the C2f_DWR module with a regional-semantic dual residual structure for efficient multi-scale contextual information capture.
  • Constructs the DroneRoadVehicles dataset with 1028 infrared images featuring complex occlusions and multi-scale targets.

Main Results:

  • YOLO-HVS achieved mAP50 of 83.4% on DroneVehicle and 97.8% on the DroneRoadVehicles dataset.
  • Demonstrated improved performance over baseline YOLOv8 with minimal increases in model parameters and GFLOPs.
  • Exhibited enhanced robustness in detecting targets under severe occlusion and low SNR.

Conclusions:

  • The proposed YOLO-HVS algorithm effectively addresses background interference and enhances multi-scale feature extraction for infrared small target detection.
  • The algorithm offers improved robustness and efficiency for real-time applications.
  • The developed DroneRoadVehicles dataset provides valuable data for advancing research in this field.