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Object Detection in Adverse Weather Conditions using Tightly-coupled Data-driven Multi-modal Sensor Fusion

  • Supervisors:
    • Prof. Dr. Ing- Sebastian Houben
    • M.Sc. Santosh Thoduka

Motivation

  • Why multi-modal sensor fusion?
Figure 1: Sensors modality characteristics Figure 2: Sensors modality characteristics
Figure 1: Sensors modality characteristics Figure 2: Sensors modality characteristics

Report and Presentation:

Datasets

Table 3.1: Multimodal adverse weather conditions datasets. Sensors†: C-R-L-N-F denotes Camera, Radar, LiDAR, Near-infrared, and Far-infrared sensors, respectively. Weather conditions‡: F-SN-R-O-SL-N denotes Fog, Snow, Rain, Overcast, Sleet, and Night conditions, respectively. Note that DENSE and nuScenes datasets are used for the project.

  • Sorted in ascending order w.r.t year column
Name Sensors† Weather Cond.‡ Size (GB) Year Citation Cnt. Link Publisher Pros Cons
DENSE CRLNF F, SN, R, N 582 2020 269 Link Mercedes, Ulm, Princeton - More adverse weather data
- Higher resolution data than nuScenes
- Less label frames
- Sparse Radar data
nuScenes CRL R, N 400 2020 3459 Link Motional - Well documented
- Heavily used
- Not good for adverse weather conditions
- Sparse Radar data
The Oxford RobotCar CRL R, SN, F 4700 2020 317 Link Oxford Robotics Institute
EU Long-term CRL SN, R, O, N 2020 72 Link University of Technology of Belfort-Montbéliard (UTBM)
RADIATE CRL F, SN, R, O, SL, N 2021 132 Link Heriot-Watt University
K-Radar CRL F, R, SN 13000 2022 15 Link KAIST University - Includes 4D radar - Heavy dataset, order via physical drive
Boreas CRL SN, R, O, N 4400 2022 38 Link University of Toronto - High resolution radar
aiMotive CRL R, O, N 85 2023 3 Link aiMotive company - Fog and Snow not included (future work)
- Relatively small dataset

Methods

Table 3.4: Multi-modal sensor fusion methods. Sensors†: C-R-L denote Camera, Radar, and LiDAR sensors, respectively

  • SAF-FCOS, HRFuser, and MT-DETR methods are thoroughly analyzed in the report
Name Sensors† Dataset Used Fusion method 2D/3D Code Link Year Published at Cited By Comment 1 Comment 2 Framework
CRF Net CR nuScenes Data-level 2D Link 2019 SDF 208 Uses BlackIn method for training didn't mention NDS Tensorflow
SAF-FCOS CR nuScenes Feature-level 2D Link 2020 Sensors 105 New spatial fusion strategy AP = 72.4, didn't mention NDS PyTorch
BIRANet CR nuScenes Feature-level 2D Link 2020 ICIP 36 PyTorch
GRIF Net CR nuScenes NA NA 2020 NA
SeeingThroughFog CRLNF DENSE Feature-level 2D NA 2020 CVPR 236 Novel entropy based net normal to adverse weather transfer NA
YOdar CR nuScenes 2D NA 2020 ICAART 23 NA
CenterFusion CR nuScenes Feature-level 3D Link 2021 WACV 170 data augmentation applied NDS = 44.0 PyTorch
RODNet CR CRUW Feature-level 2D Link 2021 WACV 58 Uses unique Radar data processing PyTorch
CRAMNet CR RADIATE NA 2022 NA
Attention Powered- #1 CR nuScenes NA NA 2022 NA
Attention Powered- #2 CR RADIATE 2D NA 2022 CISDS 0 Outperform SAF-FCOS, CenterFusion NA
MT-DETR CRL DENSE Mixed-level 2D Link 2023 WACV 2 Attention based method PyTorch
RTNH R K-Radar 3D Link 2023 NeurIPS 9 Baseline method uses only radar 4D radar dataset with AW PyTorch
HVDetFusion CR nuScenes 3D Link 2023 2 NDS = 67.4, built on top of CenterFusion PyTorch
REDFormer CR nuScenes 3D Link 2023 ITSC 0 NDS = 48.6, multi camera input, BEV based how did they define SOTA in low visibility subset? PyTorch
RADIANT CR nuScenes Feature-level 3D Link 2023 AAAI 2 didn't mention NDS How come this is SOTA? PyTorch
HRFuser CRL nuScenes, DENSE Mixed-level 2D Link 2023 ITSC 8 Mixed Fusion, Transformer based didn't mention NDS PyTorch
CamRaDepth CR nuScenes Link 2023 Not yet published PyTorch
AutoFed CRL The Oxford RobotCar NA 2023 NA
aiMotive LR aiMotive 3D Link 2023 ICLR 2 Yet to explore for Camera+Radar fusion PyTorch

Figures from the report:

  • A few sample figures highlighting the importance of multi-modal sensor fusion
drawing
Figure 3: Van occluded by a water droplet on the lens
drawing
Figure 4: LiDAR performance test
drawing
Figure 5: 1st row: clear weather condition, 2nd row: with fog. Shows that lidar affects by the fog but radar intensity remains the same
drawing
Figure 6: Highlighting the significance of fusing multimodal sensor data
drawing
Figure 7: Samples of K-Radar datasets for various weather conditions

TODOs:

  • Quantitative results
  • Qualitative results
  • Methods table
  • Dataset used table
  • Link to final report
  • Add project presentation

Contact:

Email 📧: [email protected]

Citation:

@unpublished{RnDPatel,
    abstract = {In the field of autonomous vehicles, object detection is a critical component, especially in perceiving the environment under adverse weather conditions. Traditional methods, primar- ily focused on camera data, face significant limitations in such scenarios. This research aims to address these challenges through the exploration of multimodal sensor fusion, incor- porating Cameras, LiDAR, and Radar, to improve detection accuracy in inclement weather. The study primarily focuses on a tightly-coupled fusion approach, contrasted against the existing middle fusion strategy, with experiments conducted using the nuScenes and DENSE datasets, the latter featuring extreme weather conditions. The findings indicate that the integration of complementary sensors substantially enhances detection accuracy across various weather conditions and that the tightly-coupled fusion approach outperforms the middle fusion method. Both qualitative and quantitative analyses support these conclusions, highlighting the effectiveness of this approach in the advancement of object detection technologies in autonomous vehicles. This research provides significant insights into the robustness of sensor fusion techniques, offering substantial contributions to the fields of computer vision and autonomous vehicle technology.},
    title = {Object detection in adverse weather conditions using tightly-coupled data-driven multi-modal sensor fusion},
    author = {Patel, Kevin},
    year = {2023},
    month = {December},
}