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MST-YOLO: Small Object Detection Model for Autonomous Driving.

Mingjing Li1, Xinyang Liu1, Shuang Chen2

  • 1College of Electronic Information Engineering, Changchun University, Changchun 130022, China.

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|November 27, 2024
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Summary
This summary is machine-generated.

The new MST-YOLOv8 model significantly improves autonomous vehicle safety by enhancing small object detection. This advanced system reduces missed detections, crucial for navigating public transportation environments.

Keywords:
YOLOv8 algorithmautonomous drivingsmall object detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Autonomous vehicles require precise environmental perception for safe operation.
  • Detecting small, distant objects is a major challenge in autonomous driving.
  • Existing models often struggle with the accurate identification of these critical targets.

Purpose of the Study:

  • To develop an enhanced object detection model for autonomous vehicles.
  • To specifically improve the detection of small and distant objects.
  • To increase the overall safety and reliability of autonomous driving systems.

Main Methods:

  • Introduction of the MST-YOLOv8 model, integrating C2f-MLCA and ST-P2Neck structures.
  • Incorporation of mixed local channel attention (MLCA) into the C2f structure for focused attention on small objects.
  • Addition of a P2 detection layer with scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules for improved localization.

Main Results:

  • MST-YOLOv8 achieved a 3.43% increase in precision (P) and an 8.15% increase in recall (R).
  • Demonstrated an 8.42% rise in mAP_0.5 and a significant 18.47% reduction in missed detection rate.
  • Showcased a 70.97% improvement in small object detection AP and a 68.92% increase in AR.

Conclusions:

  • The MST-YOLOv8 model offers superior performance in detecting small objects compared to the original YOLOv8.
  • The proposed enhancements are vital for advancing the capabilities of autonomous vehicles in complex environments.
  • This research contributes to the development of safer and more robust autonomous driving systems.