Informative Data Selection With Uncertainty for Multimodal Object Detection.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Noise significantly degrades object detection performance by confusing model reasoning and reducing data informativeness.
• Inaccurate recognition due to pattern shifts necessitates robust generalization in vision models.
• Developing general vision models requires deep learning approaches that can adaptively select valid information from multimodal data.

Purpose of the Study:

• To propose a universal uncertainty-aware multimodal fusion model for robust object detection.
• To address the challenges of noise interference and data chaos in multimodal learning.
• To improve the accuracy and reliability of object detection systems in adverse conditions.

Main Methods:

• A multipipeline, loosely coupled architecture integrating features and results from point clouds and images.
• Modeling uncertainty (inverse of data information) in different modalities to quantify correlations.
• Embedding uncertainty quantification into bounding box generation to reduce fusion randomness.

Main Results:

• The proposed fusion model demonstrates significant resistance to severe noise interference, including Gaussian noise, motion blur, and frost.
• Experiments on the KITTI 2-D object detection dataset show only slight performance degradation in the presence of noise.
• Adaptive fusion strategies effectively reduce randomness and generate reliable object detection outputs.

Conclusions:

• The uncertainty-aware multimodal fusion model offers a robust solution for object detection in noisy environments.
• Adaptive information selection is crucial for mitigating chaos in multimodal data fusion.
• The findings provide valuable insights into the robustness of multimodal fusion for future research in computer vision.