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Related Experiment Video

Updated: May 5, 2026

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

Snow-CLOCs: Camera-LiDAR Object Candidate Fusion for 3D Object Detection in Snowy Conditions.

Xiangsuo Fan^1,2, Dachuan Xiao¹, Qi Li^1,3

¹School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

This summary is machine-generated.

Detecting vehicles in snow is challenging due to sensor data distortion. Snow-CLOCs, a new multi-modal algorithm, improves 3D object detection in adverse weather using enhanced image and LiDAR processing.

Keywords:

DROR InceptionNeXt YOLOv5 multi-modal object detection

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Area of Science:

Computer Vision
Artificial Intelligence
Robotics

Background:

Existing 3D object detection struggles with adverse weather, particularly snow, due to sensor data distortion (LiDAR, cameras).
Single-sensor methods and current multi-modal approaches face challenges with data distortion and alignment accuracy, hindering reliable object detection.

Purpose of the Study:

To develop a robust multi-modal object detection algorithm, Snow-CLOCs, specifically designed for challenging snowy conditions.
To enhance feature extraction and reduce data quality dependency in image-based detection.
To improve LiDAR point-cloud processing by reducing noise for greater accuracy.

Main Methods:

Enhanced YOLOv5 with InceptionNeXt for image feature extraction and Wise-IoU to mitigate data quality issues.
Improved SECOND algorithm for LiDAR detection, incorporating the DROR filter to remove noise.
Fusion of camera and LiDAR detection results using sparse tensors and 2D convolutional neural networks for unified object detection and localization.

Main Results:

Snow-CLOCs demonstrated significant improvements in object detection accuracy under snowy conditions.
Achieved a vehicle detection accuracy of 86.61% in snowy environments.
Successfully addressed challenges related to data distortion and low alignment accuracy in adverse weather.

Conclusions:

The proposed Snow-CLOCs algorithm offers a robust solution for 3D object detection in adverse snowy weather.
Multi-modal fusion, combined with specific enhancements for image and LiDAR data, is effective in overcoming environmental challenges.
This approach significantly advances the reliability of autonomous systems operating in challenging meteorological conditions.