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Instance Segmentation of Tea Garden Roads Based on an Improved YOLOv8n-seg Model

Author

Listed:
  • Weibin Wu

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Zhaokai He

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Junlin Li

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Tianci Chen

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Qing Luo

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Yuanqiang Luo

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Weihui Wu

    (College of Intelligent Engineering, Shaoguan University, Shaoguan 512005, China)

  • Zhenbang Zhang

    (College of Engineering, South China Agricultural University, Guangzhou 510642, China
    College of Intelligent Engineering, Shaoguan University, Shaoguan 512005, China)

Abstract

In order to improve the efficiency of fine segmentation and obstacle removal in the road of tea plantation in hilly areas, a lightweight and high-precision DR-YOLO instance segmentation algorithm is proposed to realize environment awareness. Firstly, the road data of tea gardens in hilly areas were collected under different road conditions and light conditions, and data sets were generated. YOLOv8n-seg, which has the highest operating efficiency, was selected as the basic model. The MSDA-CBAM and DR-Neck feature fusion network were added to the YOLOv8-seg model to improve the feature extraction capability of the network and the feature fusion capability and efficiency of the model. Experimental results show that, compared with the YOLOv8-seg model, the DR-YOLO model proposed in this study has 2.0% improvement in AP @0.5 and 1.1% improvement in Precision. In this study, the DR-YOLO model is pruned and quantitatively compressed, which greatly improves the model inference speed with little reduction in AP . After deploying on Jetson, compared with the YOLOv8n-seg model, the Precision of DR-YOLO is increased by 0.6%, the AP@ 0.5 is increased by 1.6%, and the inference time is reduced by 17.1%, which can effectively improve the level of agricultural intelligent automation and realize the efficient operation of the instance segmentation model at the edge.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:7:p:1163-:d:1436383
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    References listed on IDEAS

    as
    1. 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.
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