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FID-YOLO: A pedestrian detection model integrating multispectral information in complex environments.

Di Yang1,2,3, Xilong Zhang1,2,3, Peng Wang1,2,3

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.

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|March 5, 2026
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This study introduces FID-YOLO, a new pedestrian detection system that combines visible and infrared light for improved accuracy in challenging conditions like bad weather and occlusions. The enhanced system excels in complex environments, boosting safety for intelligent driving and robot navigation.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Pedestrian detection is crucial for intelligent driving, object tracking, and robot navigation.
  • Image quality significantly impacts detection precision.
  • Adverse weather, occlusions, and scale variations degrade pedestrian detection accuracy by weakening object features.

Purpose of the Study:

  • To improve pedestrian detection performance in complex environments.
  • To address challenges posed by poor image quality and object variations.

Main Methods:

  • Proposed Feature-enriched Image Detection-YOLO (FID-YOLO) model.
  • Integrated visible and infrared light information using an illumination-aware image fusion module.
  • Introduced a cascaded feature aggregation module with reparameterization and channel shuffle.
  • Developed a scale-adaptive feature detection head for YOLO.

Main Results:

  • FID-YOLO demonstrated superior performance compared to benchmark models on M3FD and LLVIP datasets.
  • Experiments validated the effectiveness of each proposed module through ablation studies.
  • The system successfully enhanced pedestrian features and improved detection in complex scenarios.

Conclusions:

  • FID-YOLO effectively improves pedestrian detection accuracy in challenging environmental conditions.
  • The integration of multi-modal information and advanced feature processing modules enhances model generalization.
  • The proposed methods offer a significant advancement for safety-critical applications like autonomous driving.