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GCPDFFNet: Small Object Detection for Rice Blast Recognition

文献类型： 外文期刊

作者： Xie, Dejin 1 ; Ye, Wei 1 ; Pan, Yining 1 ; Wang, Jiaoyu 3 ; Qiu, Haiping 3 ; Wang, Hongkai 4 ; Li, Zhaoxing 2 ; Chen, Tianhao 1 ;

作者机构： 1.Zhejiang Univ, Coll Control Sci & Engn, Hangzhou 310027, Peoples R China

2.Zhejiang Univ, Huzhou Inst, Huzhou 313000, Peoples R China

3.Zhejiang Acad Agr Sci, Hangzhou 310021, Peoples R China

4.Zhejiang Univ, Inst Biotechnol, Hangzhou 310058, Peoples R China

关键词： convolutional neural network; rice blast recognition; small object detection

期刊名称：PHYTOPATHOLOGY （ 影响因子：2.6； 五年影响因子：3.3 ）

ISSN： 0031-949X

年卷期： 2024 年 114 卷 7 期

页码：

收录情况： SCI

摘要： Early detection of rice blast disease is pivotal to ensure rice yield. We collected in situ images of rice blast and constructed a rice blast dataset based on variations in lesion shape, size, and color. Given that rice blast lesions are small and typically exhibit round, oval, and fusiform shapes, we proposed a small object detection model named GCPDFFNet (global context-based parallel differentiation feature fusion network) for rice blast recognition. The GCPDFFNet model has three global context feature extraction modules and two parallel differentiation feature fusion modules. The global context modules are employed to focus on the lesion areas; the parallel differentiation feature fusion modules are used to enhance the recognition effect of small-sized lesions. In addition, we proposed the SCYLLA normalized Wasserstein distance loss function, specifically designed to accelerate model convergence and improve the detection accuracy of rice blast disease. Comparative experiments were conducted on the rice blast dataset to evaluate the performance of the model. The proposed GCPDFFNet model outperformed the baseline network CenterNet, with a significant increase in mean average precision from 83.6 to 95.4% on the rice blast test set while maintaining a satisfactory frames per second drop from 147.9 to 122.1. Our results suggest that the GCPDFFNet model can accurately detect in situ rice blast disease while ensuring the inference speed meets the real-time requirements.

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