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Computer vision is important for improving Intelligent Transportation Systems, especially in a real-time application to detect and identify pedestrians in moving traffic and different weather conditions. This paper introduces an effective detection technique which combines the YOLOv11 model with a Frequency-Focused Convolutional Module (FFCM) as a pedestrian-localising spatial-frequency feature extraction method. The systematic experiments of several variations of the YOLO family-lightweight (YOLO11n) to high-capacity (YOLO11x)-on various urban scenes of CrowdHuman and CityPersons datasets - are conducted. The proposed YOLO + FFCM has been proven to achieve more accuracy, with the highest Average precision (AP) and minimal miss rate, at real-time inference speeds. The experimental results emphasize the performance trade-offs to observe in terms of detection accuracy and computational cost in the various variants of the model. This paper will focus on the applicability of frequency-conscious pedestrian detectors in practical applications and future work is introduced to optimize detection networks under low-light and other limited-resource scenarios.

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YOLO-FFCM: Real-Time Pedestrian Detection in Traffic Scenes Using YOLO and Feature Fusion Context Module

  • 会议论文

  • 发表日期：

    2026年

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  • Banoth Thulasya Naik,
  • Deepak Kumar Jain,
  • Marco Leo

摘要

Computer vision is important for improving Intelligent Transportation Systems, especially in a real-time application to detect and identify pedestrians in moving traffic and different weather conditions. This paper introduces an effective detection technique which combines the YOLOv11 model with a Frequency-Focused Convolutional Module (FFCM) as a pedestrian-localising spatial-frequency feature extraction method. The systematic experiments of several variations of the YOLO family-lightweight (YOLO11n) to high-capacity (YOLO11x)-on various urban scenes of CrowdHuman and CityPersons datasets - are conducted. The proposed YOLO + FFCM has been proven to achieve more accuracy, with the highest Average precision (AP) and minimal miss rate, at real-time inference speeds. The experimental results emphasize the performance trade-offs to observe in terms of detection accuracy and computational cost in the various variants of the model. This paper will focus on the applicability of frequency-conscious pedestrian detectors in practical applications and future work is introduced to optimize detection networks under low-light and other limited-resource scenarios.