Exploring RGB+Depth Fusion for Real-Time Object Detection

Sensors
Tanguy OphoffToon Goedemé

Abstract

In this paper, we investigate whether fusing depth information on top of normal RGB data for camera-based object detection can help to increase the performance of current state-of-the-art single-shot detection networks. Indeed, depth sensing is easily acquired using depth cameras such as a Kinect or stereo setups. We investigate the optimal manner to perform this sensor fusion with a special focus on lightweight single-pass convolutional neural network (CNN) architectures, enabling real-time processing on limited hardware. For this, we implement a network architecture allowing us to parameterize at which network layer both information sources are fused together. We performed exhaustive experiments to determine the optimal fusion point in the network, from which we can conclude that fusing towards the mid to late layers provides the best results. Our best fusion models significantly outperform the baseline RGB network in both accuracy and localization of the detections.

References

Dec 18, 2007·IEEE Transactions on Pattern Analysis and Machine Intelligence·Heiko Hirschmüller
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Piotr DollarPietro Perona
Aug 7, 2018·Resources, Conservation, and Recycling·Paul VanegasJoost R Duflou

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Methods Mentioned

BETA
RGBD

Software Mentioned

OpenCV
Kinect
PyTorch
YOLOv2
Lightnet
SSD
YOLO

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