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Drivable Agricultural Road Region Detection Based on Pixel-Level Segmentation with Contextual Representation Augmentation

Author

Listed:
  • Yefeng Sun

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Liang Gong

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Wei Zhang

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Bishu Gao

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yanming Li

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Chengliang Liu

    (School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene.

Suggested Citation

  • Yefeng Sun & Liang Gong & Wei Zhang & Bishu Gao & Yanming Li & Chengliang Liu, 2023. "Drivable Agricultural Road Region Detection Based on Pixel-Level Segmentation with Contextual Representation Augmentation," Agriculture, MDPI, vol. 13(9), pages 1-13, September.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1736-:d:1231114
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    Cited by:

    1. Weibin Wu & Zhaokai He & Junlin Li & Tianci Chen & Qing Luo & Yuanqiang Luo & Weihui Wu & Zhenbang Zhang, 2024. "Instance Segmentation of Tea Garden Roads Based on an Improved YOLOv8n-seg Model," Agriculture, MDPI, vol. 14(7), pages 1-24, July.

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