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

Q2 Agricultural and Biological Sciences

Agriculture Pub Date : 2024-07-16 DOI:10.3390/agriculture14071163

Weibin Wu, Zhaokai He, Junlin Li, Tianci Chen, Qing Luo, Yuanqiang Luo, Weihui Wu, Zhenbang Zhang

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引用次数: 0

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.

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基于改进的 YOLOv8n-seg 模型的茶园道路实例分割

为了提高丘陵地区茶园道路的精细分割和障碍物清除效率，提出了一种轻量级、高精度的 DR-YOLO 实例分割算法，以实现环境感知。首先，采集不同路况和光照条件下的丘陵地区茶园道路数据，生成数据集。选择运行效率最高的 YOLOv8n-seg 作为基本模型。在 YOLOv8-seg 模型中加入了 MSDA-CBAM 和 DR-Neck 特征融合网络，以提高网络的特征提取能力和模型的特征融合能力和效率。实验结果表明，与 YOLOv8-seg 模型相比，本研究提出的 DR-YOLO 模型在 AP@0.5 和精确度方面分别提高了 2.0% 和 1.1%。本研究对 DR-YOLO 模型进行了剪枝和定量压缩，大大提高了模型的推理速度，但 AP 降低不多。在 Jetson 上部署后，与 YOLOv8n-seg 模型相比，DR-YOLO 的 Precision 提高了 0.6%，AP@0.5 提高了 1.6%，推理时间缩短了 17.1%，可有效提高农业智能自动化水平，实现实例分割模型在边缘的高效运行。

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来源期刊

Agriculture Agricultural and Biological Sciences-Horticulture

CiteScore

1.90

自引率

0.00%

发文量

审稿时长

11 weeks

期刊介绍： The Agriculture (Poľnohospodárstvo) is a peer-reviewed international journal that publishes mainly original research papers. The journal examines various aspects of research and is devoted to the publication of papers dealing with the following subjects: plant nutrition, protection, breeding, genetics and biotechnology, quality of plant products, grassland, mountain agriculture and environment, soil science and conservation, mechanization and economics of plant production and other spheres of plant science. Journal is published 4 times per year.