数字农科院2.0

Wheat Full-Width harvesting navigation line extraction method using improved Swin-Transformer

文献类型： 外文期刊

作者： Gong Cheng;Chengqian Jin;Man Chen;Zeyu Cai;Zheng Liu

作者机构：

关键词： Deep learning;Harvest;Image Segmentation;Intelligent harvesting;Visual Navigation

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2025 年 239 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： To address the challenges of insufficient accuracy and low efficiency in full-width harvesting during autonomous harvester navigation, this study proposes a lightweight image segmentation model based on an improved U-Net architecture with enhanced Swin-Transformer modules. The symmetric encoder-decoder network integrates three refined Swin-Transformer blocks in both pathways, coupled with skip connections to preserve spatial features during downsampling and upsampling. Trained and validated on a dataset of wheat field images captured by a ZED stereo camera, the model achieved state-of-the-art performance with 95.49 % mean Intersection over Union (MIoU), 98.38 % Mean Pixel Accuracy (MPA), and real-time processing at 55.5 frames per second (FPS), while maintaining exceptional computational efficiency (0.68 M parameters, 19.788 GFLOPs). Comparative experiments with DeeplabV3 + and conventional U-Net variants demonstrated superior segmentation accuracy and operational efficiency. Navigation line extraction using region overlapping and polygon midpoint fitting methods yielded an average deviation error of 6.46 pixels, confirming the model's practical applicability for precision full-width harvesting in agricultural automation.

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