GCF-DeepLabv3+: An Improved Segmentation Network for Maize Straw Plot Classification

文献类型： 外文期刊

第一作者： Liu, Yuanyuan

作者： Liu, Yuanyuan;Zhang, Jiaxin;Liu, Yuanyuan;Zhang, Jiaxin;Wang, Yueyong;Wang, Jun;Luo, Yang;Sui, Pengxiang;Ren, Ying;Liu, Xiaodan

作者机构：

关键词： conservation tillage; DeepLabv3+; remote sensing; straw coverage; semantic segmentation

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 5 期

页码：

收录情况： SCI

摘要： To meet the need of rapid identification of straw coverage types in conservation tillage fields, we investigated the use of unmanned aerial vehicle (UAV) low-altitude remote sensing images for accurate detection. UAVs were used to capture images of conservation tillage farmlands. An improved GCF-DeepLabv3+ model was utilized for detecting straw coverage types. The model incorporates StarNet as its backbone, reducing parameter count and computational complexity. Furthermore, it integrates a Multi-Kernel Convolution Feedforward Network with Fast Fourier Transform Convolutional Block Attention Module (MKC-FFN-FTCM) and a Gated Conv-Former Block (Gated-CFB) to improve the segmentation of fine plot details. Experimental results demonstrate that GCF-DeepLabv3+ outperforms other methods in segmentation accuracy, computational efficiency, and model robustness. The model achieves a parameter count of 3.19M and its FLOPs (Floating Point Operations) is 41.19G, with a mean Intersection over Union (MIoU) of 93.97%. These findings indicate that the proposed GCF-DeepLabv3+-based rapid detection method offers robust support for straw return detection.

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