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

Yefeng Sun, Liang Gong (), Wei Zhang, Bishu Gao, Yanming Li and Chengliang Liu
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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
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

Agriculture, 2023, vol. 13, issue 9, 1-13

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.

Keywords: semi-structured road detection; contextual representation; pixel-level segmentation; agricultural robot (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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