Open Access

ARTICLE

CFH-Net: Transformer-Based Unstructured Road-Free Space Detection Network

Jingcheng Yang¹, Lili Fan², Hongmei Liu^1,*

1 School of Science, Dalian Minzu University, Dalian, 116000, China
2 School of Automation, Beijing Institute of Technology, Beijing, 100081, China

* Corresponding Author: Hongmei Liu. Email:

Computers, Materials & Continua 2025, 83(3), 4725-4740. https://doi.org/10.32604/cmc.2025.062963

Received 31 December 2024; Accepted 01 March 2025; Issue published 19 May 2025

Abstract

With the advancement of deep learning in the automotive domain, more and more researchers are focusing on autonomous driving. Among these tasks, free space detection is particularly crucial. Currently, many model-based approaches have achieved autonomous driving on well-structured urban roads, but these efforts primarily focus on urban road environments. In contrast, there are fewer deep learning methods specifically designed for off-road traversable area detection, and their effectiveness is not yet satisfactory. This is because detecting traversable areas in complex outdoor environments poses significant challenges, and current methods often rely on single-image inputs, which do not align with contemporary multimodal approaches. Therefore, in this study, we propose a CFH-Net model for off-road traversable area detection. This model employs a Transformer architecture to enhance its capability of capturing global information. For multimodal feature extraction and fusion, we integrate the CM-FRM module for feature extraction and introduce the novel FFX module for feature fusion, thereby improving the perception capability of autonomous vehicles on unstructured roads. To address upsampling, we propose a new convolution precorrection method to reduce model parameters and computational complexity while enhancing the model’s ability to capture complex features. Finally, we conducted experiments on the ORFD off-road dataset and achieved outstanding results. The code is available at: https://github.com/qka1991/CFH-Net (accessed on 11 January 2025).

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Keywords

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