Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix

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Video file (mp4)

The gist

The gist The study presents a novel approach leveraging Instruct Pix2Pix to develop prompting methodologies that generate realistic datasets with weather-based augmentations aiming to mitigate the

In short

The study uses Instruct Pix2Pix to create weather-based data augmentations for object detection models like Faster R-CNN and YOLOv10. This method generates realistic images under adverse conditions to test and improve the robustness of these systems. Results show that this augmentation significantly enhances model performance in both simulated and real-world driving scenarios.

Key concepts

Instruct Pix2Pix
A diffusion model that uses text prompts to guide image generation, allowing users to edit images based on specific instructions. In this research, it is used to generate realistic weather effects by applying sequential prompts to existing images, creating new training data for object detection models.
Data Augmentation
The process of artificially increasing the size and diversity of a dataset by creating modified versions of existing data. Here, it involves using Instruct Pix2Pix to create synthetic images with specific adverse weather (like rain or night) to make the object detection models more resilient to challenging real-world conditions.
Object Detection Models
AI systems designed to locate and classify objects within images or videos, such as Faster R-CNN and YOLOv10. These models are tested for their ability to maintain accurate performance when faced with unpredictable visual challenges caused by adverse weather in autonomous driving.
Robustness
The ability of an object detection system to maintain high accuracy and reliable performance even when encountering unexpected or challenging inputs, such as poor lighting or heavy rain. The research aims to increase the robustness of these models so they can safely operate in unpredictable environmental scenarios.

Terminology used across episodes

This episode discusses

The paper

Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix · Read on arXiv

Unai Gurbindo, Axel Brando, Jaume Abella, Caroline Konig

High-Performance Embedded Systems Lab, Barcelona Supercomputing Center, Barcelona, Spain · Computer Science Dept., Univ. Politecnica de Catalunya, IDEAI-UPC- Research Center

Enhancing the robustness of object detection systems under adverse weather conditions is crucial for the advancement of autonomous driving technology. This study presents a novel approach leveraging the diffusion model Instruct Pix2Pix to develop prompting methodologies that generate realistic datasets with weather-based augmentations aiming to mitigate the impact of adverse weather on the perception capabilities of state-of-the-art object detection models, including Faster R-CNN and YOLOv10. Experiments were conducted in two environments, in the CARLA simulator where an initial evaluation of the proposed data augmentation was provided, and then on the real-world image data sets BDD100K and ACDC demonstrating the effectiveness of the approach in real environments. The key contributions of this work are twofold: (1) identifying and quantifying the performance gap in object detection models under challenging weather conditions, and (2) demonstrating how tailored data augmentation strategies can significantly enhance the robustness of these models. This research establishes a solid foundation for improving the reliability of perception systems in demanding environmental scenarios, and provides a pathway for future advancements in autonomous driving.

DOI: 10.1109/IJCNN64981.2025.11228623

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix".

Jane: Here's the gist of the paper. The gist The study presents a novel approach leveraging Instruct Pix2Pix to develop prompting methodologies that generate realistic datasets with weather-based augmentations aiming to mitigate the impact of adverse weather on the perception capabilities of state-of-the-art object detection models.

Tom: Adverse weather conditions pose critical challenges for autonomous driving technology due to their impact on sensor reliability and overall safety.

Jane: Object detection systems, such as Faster R-CNN and YOLOv10, face difficulties in collecting datasets that accurately represent these challenging scenarios.

Tom: The authors address this by proposing a data augmentation technique based on a sequential prompting strategy using diffusion models, particularly the Instruct Pix2Pix model.

Paper summary: Tom: We’ve covered the core idea of this paper, which is using Instruct Pix2Pix to generate synthetic data for training object detection models in bad weather. Now we're going to look at what they actually claim and why that research matters for autonomous driving systems >

Jane: So, the main thesis is that adverse weather conditions make it hard for object detection models like Faster R-CNN and YOLOv10 because collecting real data under those conditions is nearly impossible or very difficult >

Lu: They propose using prompting methodologies with the Instruct Pix2Pix model to generate realistic datasets with weather-based augmentations, which directly aims to mitigate the impact of adverse weather on the perception capabilities of these detection models >

Meng: I need to be clear about what this means for production. It’s not just generating pretty pictures; it’s trying to solve the data scarcity problem where we don't have enough real-world data under rain or snow conditions >

Lalam: The study is conducted in two environments, starting with the CARLA simulator for an initial assessment of the proposed data augmentation, and then moving to real-world image datasets BDD100K and ACDC to demonstrate its effectiveness in actual driving environments >

Tom: So, they are showing that this method works not just theoretically but on simulated data first, and then on real images too >

Jane: Right. The key contribution here is twofold: first, identifying and quantifying the performance gap in object detection models under challenging weather conditions, and second, demonstrating how tailored data augmentation strategies can significantly enhance the robustness of these models >

Lu: They establish a solid foundation for improving the reliability of perception systems in demanding environmental scenarios and provide a pathway for future advancements in autonomous driving >

Meng: I’m still thinking about the practical side: if this works well on BDD100K and ACDC, how much computational overhead is involved just to generate these augmented datasets?

Lalam: The paper doesn't give us the cost details for generation, but it shows that they successfully generated images under various weather conditions to analyze the sensitivity of detection systems to differing meteorological conditions >

Tom: So, the focus here isn't just on the new tool itself but proving its practical application across different testing grounds >

Jane: And that brings us right up to where we were before, with a look at how this research changes things for people who are just listening to this show >

Conclusion: Tom: So wrapping up the discussion on "Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix," it seems like the core idea is that generative AI can be a practical tool for boosting object detection robustness under bad weather >

Jane: That’s right. They show that by using diffusion models to create realistic data with weather augmentations, we can help state-of-the-art object detection models handle adverse conditions more reliably >

Lu: The authors successfully identify and quantify the performance gap in object detection models under challenging weather conditions, and they demonstrate how tailored data augmentation strategies can significantly enhance the robustness of these models >

Meng: For deployment, I think what this means is that we need to be careful about relying on synthetic data; it’s a great tool for training, but we still need rigorous testing on real traffic because the paper shows it doesn't magically solve all problems >

Lalam: The paper does flag that challenges remain regarding the manual filtering of hallucinated images, suggesting a need for future automation to move this from research into something deployable >

Tom: Exactly. They show that in the simulated environment, augmentations reduced accuracy drop when models were evaluated under challenging weather conditions for both Faster R-CNN and YOLO models >

Jane: And on real-world data, Faster-R-CNN showed an increase in robustness under adverse conditions, achieving improvements such as a zero point zero seven mAP boost for pedestrian detection in night and rain settings compared to a basic approach >

Lu: That's a concrete number from the paper. The paper also notes that YOLO models produced very similar results in real-world evaluations, suggesting that the benefit was less pronounced for those single-stage models >

Meng: So, while it’s promising, we can see this as a strong enhancement for two-stage architectures in real data right now >

Lalam: And it's important to remember that they also point out that achieving a stronger benefit for single-stage YOLO models under real-world conditions may require more specialized training schemes >

Tom: So, the conclusion is that diffusion-based image augmentations are a practical and powerful tool for enhancing object detection in adverse weather conditions, but we still have work to do on automation and scaling up the data generation process >

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