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Area of Science:

  • Computer Vision and Machine Learning
  • Artificial Intelligence for Autonomous Systems
  • Image Processing and Pattern Recognition

Background:

  • Multi-object detection in road scenes faces challenges with occluded targets and small objects like pedestrians and non-motor vehicles.
  • Existing models struggle with the accurate identification of tiny targets, impacting overall scene understanding in complex traffic environments.
  • The need for robust detection systems is critical for applications such as autonomous driving and traffic monitoring.

Purpose of the Study:

  • To propose an improved YOLOv5 model for enhanced multi-object detection in challenging road scenarios.
  • To specifically address the limitations in detecting tiny targets and occluded objects.
  • To improve the overall accuracy, recall, and mean Average Precision (mAP) of object detection models.

Main Methods:

  • An improved YOLOv5 model incorporating a multi-level aggregation feature perception (ML-AFP) mechanism.
  • Addition of a micro target detection layer and a double-head mechanism to enhance tiny target detection.
  • Utilization of Varifocal loss for improved non-maximum suppression (NMS) to handle object occlusion.
  • Adaptive fusion of multi-scale spatial features within the ML-AFP mechanism to boost feature representation.

Main Results:

  • Significant improvements in accuracy, recall rate, and mAP values were observed on benchmark datasets (KITTI, BDD100K).
  • The enhanced model demonstrated superior performance in detecting small objects, such as pedestrians and non-motor vehicles.
  • Effective handling of target occlusion was achieved through the Varifocal loss and improved NMS strategy.

Conclusions:

  • The proposed improved YOLOv5 model, featuring the ML-AFP mechanism, effectively addresses challenges in multi-object detection for crowded road scenes.
  • The integration of specialized layers and loss functions leads to substantial gains in detecting tiny and occluded objects.
  • This research contributes a more robust and accurate object detection solution for autonomous driving and intelligent transportation systems.