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A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection.

Alfredo Chávez Plascencia¹, Pablo García-Gómez², Eduardo Bernal Perez¹

¹Centre for Sensors, Instrumentation and Systems Development (CD6), Polytechnic University of Catalonia (UPC), Rambla de Sant Nebridi 10, 08222 Terrassa, Spain.

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|April 28, 2023

Summary

This study explores fusing lidar, radar, and RGB data for pedestrian detection using semantic segmentation. The multimodal approach shows high accuracy, mimicking human vision for improved autonomous driving safety.

Keywords:

autonomous driving convolutional neural networks: sensor calibration sensor fusion

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Area of Science:

Computer Vision
Sensor Fusion
Autonomous Driving Systems

Background:

Current pedestrian detection methods often rely on RGB and lidar, which struggle in scattered environments and do not mimic human visual perception.
Radar offers a complementary sensing modality to overcome limitations of lidar and vision in challenging conditions.

Purpose of the Study:

To investigate the feasibility of fusing lidar, radar, and RGB data for enhanced pedestrian detection.
To develop a multimodal sensor fusion architecture for autonomous driving applications.
To evaluate the performance of a semantic segmentation-based approach for detecting pedestrians.

Main Methods:

A fully connected convolutional neural network architecture based on SegNet was employed for pixel-wise semantic segmentation.
Lidar and radar data were transformed into 2D grayscale images, while RGB images were used with three channels.
A novel extrinsic calibration matrix method using singular value decomposition was proposed for sensor alignment.

Main Results:

The proposed fusion method achieved high performance metrics, including a training mean pixel accuracy of 99.7% and training mean intersection over union (IoU) of 99.5%.
Testing yielded a mean IoU of 94.4% and a testing pixel accuracy of 96.2%, demonstrating effectiveness despite a small custom dataset.
The model showed good performance in detecting pedestrians, even with some observed overfitting.

Conclusions:

The fusion of lidar, radar, and RGB data using semantic segmentation is a feasible and effective approach for pedestrian detection.
This multimodal strategy offers advantages in detecting pedestrians with less ambiguity, similar to human visual perception.
Further research with larger datasets is recommended to optimize training and mitigate overfitting for robust real-world deployment.