Robustness of Object Detectors in Degrading Weather Conditions

• URL: http://arxiv.org/abs/2106.08795v1
• Date: Wed, 16 Jun 2021 13:56:07 GMT
• Title: Robustness of Object Detectors in Degrading Weather Conditions
• Authors: Muhammad Jehanzeb Mirza, Cornelius Buerkle, Julio Jarquin, Michael Opitz, Fabian Oboril, Kay-Ulrich Scholl, Horst Bischof
• Abstract summary: State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions. These systems need to work in degrading weather conditions, such as rain, fog and snow. Most approaches evaluate only on the KITTI dataset, which consists only of clear weather scenes.
• Score: 7.91378990016322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art object detection systems for autonomous driving achieve promising results in clear weather conditions. However, such autonomous safety critical systems also need to work in degrading weather conditions, such as rain, fog and snow. Unfortunately, most approaches evaluate only on the KITTI dataset, which consists only of clear weather scenes. In this paper we address this issue and perform one of the most detailed evaluation on single and dual modality architectures on data captured in real weather conditions. We analyse the performance degradation of these architectures in degrading weather conditions. We demonstrate that an object detection architecture performing good in clear weather might not be able to handle degrading weather conditions. We also perform ablation studies on the dual modality architectures and show their limitations.

Related papers

• WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning [69.82211470647349]
  We introduce the first generalist weather foundation model (WeatherGFM) It addresses a wide spectrum of weather understanding tasks in a unified manner. Our model can effectively handle up to ten weather understanding tasks, including weather forecasting, super-resolution, weather image translation, and post-processing.
  arXiv  Detail & Related papers  (2024-11-08T09:14:19Z)
• Advancing Meteorological Forecasting: AI-based Approach to Synoptic Weather Map Analysis [3.686808512438363]
  Our study proposes a novel preprocessing method and convolutional autoencoder model to improve the interpretation of synoptic weather maps. This model could recognize historical synoptic weather maps that nearly match current atmospheric conditions.
  arXiv  Detail & Related papers  (2024-11-08T07:46:50Z)
• Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models [0.08271752505511926]
  Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. Recently released suite of AI-based weather models produces medium-range forecasts within seconds. We assess the forecast skill of three top-performing AI-models for convective parameters against reanalysis and ECMWF's operational numerical weather prediction model IFS.
  arXiv  Detail & Related papers  (2024-06-13T07:46:03Z)
• DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions [2.048226951354646]
  We present an unsupervised domain adaptation framework for object detection in adverse weather conditions. Our method resolves the style gap by concentrating on style-related information of high-level features. Using self-supervised contrastive learning, our framework then reduces the weather gap and acquires instance features that are robust to weather corruption.
  arXiv  Detail & Related papers  (2023-09-15T04:37:28Z)
• Robust Monocular Depth Estimation under Challenging Conditions [81.57697198031975]
  State-of-the-art monocular depth estimation approaches are highly unreliable under challenging illumination and weather conditions. We tackle these safety-critical issues with md4all: a simple and effective solution that works reliably under both adverse and ideal conditions.
  arXiv  Detail & Related papers  (2023-08-18T17:59:01Z)
• Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal [97.53040662243768]
  We propose a CLIP embedding module to make the network handle different weather conditions adaptively. This module integrates the sample specific weather prior extracted by CLIP image encoder together with the distribution specific information learned by a set of parameters.
  arXiv  Detail & Related papers  (2023-06-15T10:06:13Z)
• Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions [16.014293219912]
  In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. We propose Domain-Incremental Learning through Activation Matching (DILAM) to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector.
  arXiv  Detail & Related papers  (2023-05-30T11:37:41Z)
• Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing [70.16043883381677]
  In autonomous racing, the weather can change abruptly, causing significant degradation in perception, resulting in ineffective manoeuvres. In order to improve detection in adverse weather, deep-learning-based models typically require extensive datasets captured in such conditions. We introduce an approach of using synthesised adverse condition datasets in autonomous racing (generated using CycleGAN) to improve the performance of four out of five state-of-the-art detectors.
  arXiv  Detail & Related papers  (2022-01-10T10:02:40Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Assessing out-of-domain generalization for robust building damage detection [78.6363825307044]
  Building damage detection can be automated by applying computer vision techniques to satellite imagery. Models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event. We argue that future work should focus on the OOD regime instead.
  arXiv  Detail & Related papers  (2020-11-20T10:30:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.