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An Improved YOLOv7 Lightweight Detection Algorithm for Obscured Pedestrians.

Chang Li1, Yiding Wang2, Xiaoming Liu1

  • 1College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China.

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Summary
This summary is machine-generated.

This study enhances pedestrian detection in crowded scenes by improving the YOLOv7 algorithm. The new method significantly improves the detection of obscured and small pedestrians, boosting overall accuracy.

Keywords:
attention mechanismmobilenetV3obscured pedestrianpedestrian detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Pedestrian detection algorithms often fail to identify obscured pedestrians in dense traffic due to occlusion.
  • Existing methods struggle with high pedestrian density, leading to missed detections and reduced prediction scores for occluded individuals.

Purpose of the Study:

  • To improve the detection accuracy of obscured and small pedestrians in dense traffic scenarios.
  • To enhance the performance of the YOLOv7 algorithm for real-time pedestrian detection in autonomous driving systems.

Main Methods:

  • Replaced YOLOv7's backbone with the lightweight Mobilenetv3 for faster processing.
  • Introduced a high-resolution feature pyramid structure to enhance feature extraction for occluded and small pedestrians.
  • Developed an attention-mechanism-based detection head to reduce redundant detections and improve accuracy.

Main Results:

  • Achieved a mean average precision (mAP) of 89.75% on the CrowdHuman dataset.
  • Demonstrated a 9.5 percentage point improvement over the baseline YOLOv7 algorithm.
  • Significantly enhanced detection rates for obscured and small-sized pedestrians.

Conclusions:

  • The proposed algorithm effectively addresses the challenge of detecting obscured pedestrians in dense crowds.
  • The integration of Mobilenetv3, high-resolution feature pyramid, and attention mechanism optimizes pedestrian detection performance.
  • This enhanced approach shows considerable potential for real-world applications like autonomous driving.