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Surround Sensing Technique for Trucks Based on Multi-Features and Improved Yolov5 Algorithm.

Zixian Li1, Yongtao Li1, Hanyan Li2

  • 1School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China.

Sensors (Basel, Switzerland)
|April 13, 2024
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Summary
This summary is machine-generated.

This study introduces an improved truck surround sensing technique using multi-features and an enhanced YOLOv5 algorithm. The method boosts image stitching accuracy and target recognition, ensuring safer truck driving.

Keywords:
SIFTYOLOv5corner featureimage mosaictarget location

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Area of Science:

  • Computer Vision
  • Robotics
  • Automotive Safety

Background:

  • Traditional rearview mirrors offer limited safety for trucks.
  • Existing computer vision algorithms like SIFT and YOLO have limitations in feature extraction and accuracy.
  • Enhanced surround perception is crucial for improving truck safety.

Purpose of the Study:

  • To develop an advanced truck surround sensing technique.
  • To improve the accuracy and efficiency of target recognition and image registration for trucks.
  • To enhance overall truck driving safety through better environmental perception.

Main Methods:

  • Extraction of edge corner points and infrared features from target regions.
  • Generation of a feature point set using an improved Scale-Invariant Feature Transform (SIFT) algorithm for registration.
  • Improvement of the YOLOv5 algorithm by fusing infrared features and implementing a composite prediction mechanism.

Main Results:

  • Achieved an average 17% improvement in image stitching accuracy.
  • Reduced processing time by 89% compared to traditional methods.
  • Increased target recognition accuracy by 2.86%.

Conclusions:

  • The proposed multi-feature and improved YOLOv5 technique effectively enhances truck surround perception.
  • The method accurately identifies targets, reducing both missed alarms and false alarms.
  • This approach significantly contributes to improving safety in truck operations.