Image Deraining and Denoising Convolutional Neural Network ForAutonomous Driving

TL;DR

This paper introduces a convolutional neural network designed to remove rain effects from images, significantly enhancing object detection accuracy for autonomous vehicles in rainy weather.

Contribution

The paper proposes a novel deraining CNN architecture tailored for autonomous driving, with experimental validation showing improved detection accuracy in rainy conditions.

Findings

Effective rain removal improves object detection accuracy.

The proposed method outperforms existing deraining techniques.

Ablation studies confirm the contribution of each network component.

Abstract

Perception plays an important role in reliable decision-making for autonomous vehicles. Over the last ten years, huge advances have been made in the field of perception. However, perception in extreme weather conditions is still a difficult problem, especially in rainy weather conditions. In order to improve the detection effect of road targets in rainy environments, we analyze the physical characteristics of the rain layer and propose a deraining convolutional neural network structure. Based on this network structure, we design an ablation experiment and experiment results show that our method can effectively improve the accuracy of object detection in rainy conditions.