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Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions.

Imran Ashraf1, Soojung Hur1, Gunzung Kim2

  • 1Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary

The YOLOx-s model demonstrates strong performance in detecting vehicles during adverse weather conditions like snow, fog, rain, and sandstorms. Image enhancement techniques further improve its accuracy for autonomous vehicle perception.

Keywords:
YOLOxautonomous vehiclesdeep learningimage processingvehicle detection

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Autonomous Systems

Background:

  • Autonomous vehicle development relies heavily on accurate on-road vehicle detection.
  • Challenging environmental conditions (rain, fog, snow, sandstorms) significantly impede vehicle detection accuracy.
  • Real-time performance is crucial for autonomous vehicle safety and functionality.

Purpose of the Study:

  • To investigate the effectiveness of the YOLOx object detection model for identifying vehicles in adverse weather conditions.
  • To evaluate different YOLOx variants (YOLOx-s, YOLOx-m, YOLOx-l) on a diverse dataset.
  • To explore the impact of image enhancement on YOLOx performance in challenging weather.

Main Methods:

  • Utilized the YOLOx object detection model, specifically its 's', 'm', and 'l' variants.
  • Tested the model on the DAWN benchmark dataset, which includes images from various adverse weather scenarios.
  • Evaluated model performance using precision, recall, and mean Average Precision (mAP).
  • Conducted experiments with multiscale retinex image enhancement.

Main Results:

  • The YOLOx-s variant achieved superior performance compared to YOLOx-m and YOLOx-l.
  • YOLOx-s demonstrated high mAP scores across all tested weather conditions: 0.8983 (snow), 0.8656 (sandstorms), 0.9509 (rain), and 0.9524 (fog).
  • Performance was notably better in snow and fog compared to rain and sandstorms.
  • Multiscale retinex image enhancement positively impacted YOLOx performance.

Conclusions:

  • YOLOx-s is a capable model for real-time vehicle detection in adverse weather conditions.
  • Image quality enhancement is a viable strategy to further improve detection accuracy in challenging environments.
  • The findings contribute to the advancement of robust perception systems for autonomous vehicles.