Feature Map Transformation for Multi-sensor Fusion in Object Detection Networks for Autonomous Driving
Autor: | Julien Vitay, Enrico Schroder, Mirko Mählisch, Sascha Braun, Fred H. Hamker |
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Rok vydání: | 2019 |
Předmět: |
Fusion scheme
020203 distributed computing Fusion business.industry Computer science 02 engineering and technology Modular design Sensor fusion Object detection Extractor Multi sensor Lidar 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030177973 |
DOI: | 10.1007/978-3-030-17798-0_12 |
Popis: | We present a general framework for fusing pre-trained object detection networks for multiple sensor modalities in autonomous cars at an intermediate stage. The key innovation is an autoencoder-inspired Transformer module which transforms perspective as well as feature activation characteristics from one sensor modality to another. Transformed feature maps can be combined with those of a modality-native feature extractor to enhance performance and reliability through a simple fusion scheme. Our approach is not limited to specific object detection network types. Compared to other methods, our framework allows fusion of pre-trained object detection networks and fuses sensor modalities at a single stage, resulting in a modular and traceable architecture. We show effectiveness of the proposed scheme by fusing camera and Lidar information to detect objects using our own as well as the KITTI dataset. |
Databáze: | OpenAIRE |
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