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Summary
This summary is machine-generated.

This study introduces PVformer, an advanced algorithm for detecting pedestrians and vehicles in autonomous driving, even in rainy conditions. It enhances image quality and detection accuracy by integrating a deraining module with a Transformer-based model.

Keywords:
Swin Transformerimage derainingpedestrian and vehicle detection

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Area of Science:

  • Computer Vision
  • Autonomous Driving Systems
  • Deep Learning

Background:

  • Accurate pedestrian and vehicle detection is crucial for autonomous vehicle safety.
  • Transformer-based object detection shows promise but struggles with adverse weather like rain.
  • Rainfall significantly degrades image quality, challenging current detection algorithms.

Purpose of the Study:

  • To develop an end-to-end algorithm for robust pedestrian and vehicle detection in rainy conditions.
  • To improve the accuracy and reliability of object detection systems for autonomous driving in adverse weather.
  • To mitigate the impact of rain streak occlusion on detection performance.

Main Methods:

  • Proposed PVformer, an algorithm based on the Swin Transformer with an integrated deraining module.
  • Introduced a four-branch feature mapping model using Transformer blocks for single-image deraining.
  • Designed a local enhancement perception block combining CNN and Transformer to address small object detection challenges.
  • Employed transfer learning to train the combined deraining and detection modules.

Main Results:

  • The PVformer algorithm demonstrated strong performance in rainy scenarios.
  • Significantly improved the accuracy of pedestrian and vehicle detection under rainy conditions.
  • The integrated deraining module effectively reduced the influence of rain streak occlusion.

Conclusions:

  • The proposed PVformer algorithm enhances image quality and detection accuracy in rainy scenes for autonomous driving.
  • The novel approach effectively addresses the challenges of object detection in adverse weather.
  • PVformer offers a promising solution for improving the safety and reliability of self-driving vehicles.