Rothmeier, Thomas (University of Applied Sciences Ingolstadt), Dal Vesco Hoger, Mayara (The Federal University of Technology – Parana (UTFPR)), Nassu, Bogdan Tomoyuki (Federal University of Technology - Parana), Huber, Werner (Technische Hochschule Ingolstadt - CARISSMA Institute of Automat), Knoll, Alois (Technische Universität München)

Out of the Box: Weather Augmentation for Enhanced Detection in Bad Visibility Conditions

Scheduled for presentation during the Poster Session "Perception - Road and weather conditions" (WeBT16), Wednesday, September 25, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Accurately detecting vehicles, pedestrians, and obstacles is crucial for the decision-making capabilities of autonomous vehicles. Although current methodologies demonstrate high detection accuracy under favorable environmental conditions, their performance significantly diminishes in bad visibility, such as fog, rain, or snow. This is due, at least in part, to the fact that these "edge-case" scenarios are underrepresented in current datasets. This work introduces a novel approach that utilizes state-of-the-art diffusion models and Generative Adversarial Networks to artificially enhance clear weather images with simulated weather disturbances. In addition, we present a method for eliminating bounding boxes that become invalid due to severe weather conditions or image deformations caused by the augmentations. The effectiveness of our methods was assessed both qualitatively and quantitatively across a broad dataset under varied weather scenarios. The results demonstrate that our image augmentation techniques can enhance object detection performance, surpassing the clear weather baseline. Our approach for removing invalid bounding boxes consistently improves Average Precision by 2-3% across most tested augmentations and is most effective for improving the detection of small vehicles.