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Research on Deep Learning Automatic Vehicle Recognition Algorithm Based on RES-YOLO Model.

Yanyi Li1, Jian Wang2, Jin Huang3

  • 1College of Surveying and Geo-Informatics, Tongji Univesity, Shanghai 200092, China.

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Summary

This study introduces an improved RES-YOLO algorithm for automatic vehicle detection, significantly reducing errors and improving accuracy in autonomous driving systems. The enhanced model offers better performance compared to existing algorithms, even in challenging environments.

Keywords:
YOLOadaptive loss functionautomatic drivingdeep learningtarget recognitionvehicle detection model

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Autonomous Systems

Background:

  • Traditional vision measurement and remote sensing face challenges in automatic vehicle discrimination, high missing rates for multiple targets, and environmental sensitivity.
  • Ubiquitous mapping and related technologies are increasingly integrated into autonomous driving and target recognition applications.
  • Existing object detection algorithms struggle with accurate and robust vehicle identification in complex scenarios.

Purpose of the Study:

  • To propose an improved RES-YOLO detection algorithm for enhanced automatic vehicle target detection.
  • To address limitations of traditional methods, including difficulty in discrimination, high missing rates, and environmental sensitivity.
  • To optimize the YOLO algorithm for superior performance in autonomous driving applications.

Main Methods:

  • An improved RES-YOLO detection algorithm was developed by optimizing feature networks and constructing adaptive loss functions.
  • The algorithm was trained and verified using the BDD100K dataset.
  • The enhanced YOLO deep learning model was compared against state-of-the-art target recognition algorithms like SSD and Faster-RCNN.

Main Results:

  • The RES-YOLO algorithm effectively identifies multiple vehicle targets, reducing missing and false detection rates.
  • Achieved a local optimal accuracy of up to 95% and an average accuracy above 86% on large datasets.
  • Demonstrated superior average accuracy compared to five other advanced algorithms, including SSD and Faster-RCNN.
  • Showed a 1.0% and 1.7% average accuracy improvement over the original YOLO for small and large datasets, respectively.
  • Reduced training time by 7.3% compared to the original YOLO algorithm.
  • Exhibited satisfactory recognition accuracy across five local vehicle datasets with varying interference backgrounds.

Conclusions:

  • The proposed RES-YOLO algorithm provides effective and robust vehicle target detection, outperforming existing methods.
  • The improvements in feature networks and adaptive loss functions enhance detection accuracy and efficiency.
  • The algorithm demonstrates significant potential for real-world autonomous driving applications, performing well under diverse environmental interferences.