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An Off-Road Freespace Detection Method Based on Boundary Enhancement and Geometry-Depth Prior
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Wentai Yu1, Mingda Gao2, Jingyue Wang3
Automobile Technology | 2026, (7) : 11 - 19
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Automobile Technology | 2026, (7): 11-19
• Special Topic on Object Detection and Road Lane Recognition Technologies for Autonomous Driving •
An Off-Road Freespace Detection Method Based on Boundary Enhancement and Geometry-Depth Prior
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Wentai Yu1, Mingda Gao2, Jingyue Wang3
Affiliations
  • 1.School of Automobile Engineering, Liaoning Institute of Science and Engineering, Jinzhou 121007
  • 2.School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001
  • 3.School of Automobile and Transportation, Shenyang Ligong University, Shenyang 110159
Published: 2026-07-24 doi: 10.19620/j.cnki.1000-3703.20260254
Outline
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To address the challenges of complex surface textures, blurred boundaries, and significant illumination variations in unstructured road scenarios, this paper proposes a freespace detection method using only red-green-blue (RGB) images and integrating boundary enhancement and geometry-depth prior attention. Based on the SegFormer framework, the proposed method extracts explicit boundary information through auxiliary boundary supervision and introduces boundary cues into the semantic feature space via a boundary feature fusion module, thereby enhancing the representation of soft boundaries and irregular contours. Meanwhile, a geometry-depth prior attention module is designed to incorporate geometric continuity priors and vertical pseudo-depth priors, enabling structured feature reweighting and improving prediction stability in complex terrain. Experimental results show that the proposed method achieves an IoU of 90.8% and an F1-score of 95.1% on the Off-Road Freespace Detection (ORFD) dataset, outperforming the compared monocular RGB methods on major segmentation metrics and demonstrating good cross-dataset generalization capability. These results indicate that the proposed method achieves a favorable balance among segmentation accuracy, boundary quality, and inference efficiency.

Semantic segmentation  /  Freespace detection  /  Unstructured road  /  Boundary enhancement  /  Geometry-depth prior attention
于文泰, 高明达, 王靖岳. 融合边界增强与几何-深度先验的非结构化道路可通行区域检测方法. 汽车技术, 2026 , (7) : 11 -19 . DOI: 10.19620/j.cnki.1000-3703.20260254
Wentai Yu, Mingda Gao, Jingyue Wang. An Off-Road Freespace Detection Method Based on Boundary Enhancement and Geometry-Depth Prior[J]. Automobile Technology, 2026 , (7) : 11 -19 . DOI: 10.19620/j.cnki.1000-3703.20260254
Year 2026 volume Issue 7
168
37
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doi: 10.19620/j.cnki.1000-3703.20260254
  • Online Date:2026-09-23
  • Published:2026-07-24
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  • Revised:2026-05-19
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Affiliations
    1.School of Automobile Engineering, Liaoning Institute of Science and Engineering, Jinzhou 121007
    2.School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001
    3.School of Automobile and Transportation, Shenyang Ligong University, Shenyang 110159
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