A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving

In this paper, we propose a new RGB-thermal image segmentation method based on parameter sharing and attention fusion for safe autonomous driving. An encoder-decoder network structure is adopted. The encoder, which has shared convolution layer parameters and private batch normalization layer paramet...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 25; H. 6; S. 5122 - 5137
Hauptverfasser:	Li, Guofa, Lin, Yongjie, Ouyang, Delin, Li, Shen, Luo, Xiao, Qu, Xingda, Pi, Dawei, Li, Shengbo Eben
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Autonomous driving Autonomous vehicles Convolutional neural networks Decoding Encoders-Decoders Feature extraction image fusion Image segmentation Parameters RGB image Semantic segmentation Semantics thermal image Thermal imaging Transformers
ISSN:	1524-9050, 1558-0016
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Abstract	In this paper, we propose a new RGB-thermal image segmentation method based on parameter sharing and attention fusion for safe autonomous driving. An encoder-decoder network structure is adopted. The encoder, which has shared convolution layer parameters and private batch normalization layer parameters (parameter sharing scheme), is used to extract features from RGB and thermal images. The extracted features are then fused by spatial and channel attention. The output of each residual block is fused, and the self-learning weight is used to integrate the fusion information of all residual blocks of the same levels. Subsequently, the fused features are integrated through a feature integration (FI) module in the decoder. Cross-entropy supervision of segmentation and edge is performed on the outputs of the decoders. Our proposed method is evaluated and compared with 17 state-of-the-art image segmentation methods, both qualitatively and quantitatively on the MFNet dataset which includes various objects in urban scenes. The results show that the proposed method outperforms previous methods by at least 0.3% and 1.8% in MRecall and MIoU, respectively, providing foundations for the development of autonomous driving technologies for safety enhancement.
AbstractList	In this paper, we propose a new RGB-thermal image segmentation method based on parameter sharing and attention fusion for safe autonomous driving. An encoder-decoder network structure is adopted. The encoder, which has shared convolution layer parameters and private batch normalization layer parameters (parameter sharing scheme), is used to extract features from RGB and thermal images. The extracted features are then fused by spatial and channel attention. The output of each residual block is fused, and the self-learning weight is used to integrate the fusion information of all residual blocks of the same levels. Subsequently, the fused features are integrated through a feature integration (FI) module in the decoder. Cross-entropy supervision of segmentation and edge is performed on the outputs of the decoders. Our proposed method is evaluated and compared with 17 state-of-the-art image segmentation methods, both qualitatively and quantitatively on the MFNet dataset which includes various objects in urban scenes. The results show that the proposed method outperforms previous methods by at least 0.3% and 1.8% in MRecall and MIoU, respectively, providing foundations for the development of autonomous driving technologies for safety enhancement.
Author	Li, Guofa Ouyang, Delin Lin, Yongjie Luo, Xiao Pi, Dawei Li, Shengbo Eben Li, Shen Qu, Xingda
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SubjectTerms	Autonomous driving Autonomous vehicles Convolutional neural networks Decoding Encoders-Decoders Feature extraction image fusion Image segmentation Parameters RGB image Semantic segmentation Semantics thermal image Thermal imaging Transformers
Title	A RGB-Thermal Image Segmentation Method Based on Parameter Sharing and Attention Fusion for Safe Autonomous Driving
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